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Instructions for use Title Transportation Policy for the Reduction of Social Exclusion of Low-Income and Elderly People in Bangkok Author(s) Tansawat, Tithiwach Citation 北海道大学. 博士(工学) 甲第13347号 Issue Date 2018-09-25 DOI 10.14943/doctoral.k13347 Doc URL http://hdl.handle.net/2115/71824 Type theses (doctoral) File Information Tithiwach_Tansawat.pdf Hokkaido University Collection of Scholarly and Academic Papers : HUSCAP
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Instructions for use
Title Transportation Policy for the Reduction of Social Exclusion of Low-Income and Elderly People in Bangkok
Author(s) Tansawat, Tithiwach
Citation 北海道大学. 博士(工学) 甲第13347号
Issue Date 2018-09-25
DOI 10.14943/doctoral.k13347
Doc URL http://hdl.handle.net/2115/71824
Type theses (doctoral)
File Information Tithiwach_Tansawat.pdf
Hokkaido University Collection of Scholarly and Academic Papers : HUSCAP
Transportation Policy for the Reduction of Social
Exclusion of Low-Income and Elderly People in Bangkok
バンコクにおける低所得者や高齢者の社会的疎外を減少させる
ための交通政策に関する研究
Tithiwach Tansawat
A Dissertation Submitted in Partial Fulfillment of the Requirements for the
Degree of Doctoral of Engineering
Division of Engineering and Policy for Sustainable Environment
Graduate School of Engineering
Hokkaido University
September 2018
i
ACKNOWLEDGMENTS
It would be impossible to accomplish my doctoral dissertation without the support from kind and
generous people around me. First, I would like to express my deepest gratitude to my adviser, Associate
Professor Kunihiro Kishi for his effort, support, precious guidance and kindness all the time during my
graduate studies at Hokkaido University. His advises greatly contributed to the dissertation.
I would like to express my sincerest thanks to Professor Toru Hagiwara, Professor Shinei Takano and
Professor Kenetsu Uchida for providing useful comments, as the annual evaluation committee of my
dissertation. Their precious advices led my study to the appropriate direction.
In particular, my sincerest thanks to Associate Professor Kunnawee Kanitpong and Associate Professor
Kasem Choocharukul for their helpful advices and recommendations during writing research papers
and the progress of my dissertation. Their effort significantly contributed to my paper publications.
Special thanks to the Japanese Government and Hokkaido University for providing me the financial
support (Monbukagakusho Scholarship). It made me concentrate more on my study during these three
years. Gratefully thank MAA Consultants Co., Ltd., Chulalongkorn University and Asian Institute of
Technology for their supports and corporations for data collections. Without their fund and
coordination, this study would not have been accomplished.
Finally, I am indebted to my parents, sisters, relatives, colleagues and staffs of Hokkaido University,
who generously provided inspiration, friendship and encouragement throughout my three years at the
Graduate School of Engineering, Hokkaido University.
ii
ABSTRACT
Transport disadvantage of low-mobility people, such as disability, elderly and low income, caused the
lack of mobility to travel to access social activities and service places in the city, leading to the feeling
socially excluded from their society. Although Thai government has created various transport policies
to support low-mobility persons, the evaluation of transport policy in the aspect of the reduction of
social exclusion caused by transport difficulty of low-income and elderly has been rarely focused in
Bangkok. Therefore, this study focused on the evaluation of transport policy in terms of the reduction
of social exclusion of Bangkok low income and elderly. The aim of this study is to clarify the current
situation of social exclusion caused by transport difficulty of those people and to propose the transport
policy implication to reduce their feeling of social exclusion. The content of this dissertation is
organized in 9 chapters.
Chapter 1 presents the general background, objective and dissertation overview. The necessary of
solution to solve the problem of social exclusion caused by transport difficulty of low-mobility people
and the importance of this study the are defined. In chapter 2, the definition social exclusion, dimension,
and policy in global to reduce social exclusion are explained. In chapter 3, the existing studies of
transport-related social exclusion and how to interpret the degree of social exclusion are reviewed,
especially the previous studies related to low income and elderly. Chapter 4 describes the current
situation of transportation of Bangkok, the focused area of this study. Chapter 5 shows the framework
of the study. According to the review of the previous studies and the definition of social exclusion of
this study, the frameworks of the analyses are shown from Chapter 6.
In chapter 6, free train policy was evaluated in terms of the reduction of social exclusion of Bangkok
low income. After the interviews with 392 free train users, 32.65 percent of them travel made more trip
to participate in more non-fixed-schedule activities. The binary logit models clarified that low-income
users tended to make more trip and feel less degree of social exclusion rather than non-low income did.
However, 40.31 percent of free train users was non-low income. To encourage low income people to
receive more benefit of this subsidy policy, the registration of the specific identification card given to
only low-income persons, which must be shown at the ticket booth to get free ticket, was suggested.
Chapter 7 clarified the relationship between degree of satisfaction with daily transportation and degree
of feeling social exclusion of Bangkok elderly, by logistic regression analysis and structural equation
modelling. Based on the interview with elderly, the data indicated that non-duty activities had more
influence on the feeling of social inclusion of Bangkok elderly rather than duty activities, such as going
to hospital. In the other word, low level of transport services was the social obstacle demotivating
elderly people from going out to participate in social activities and services.
In Chapter 8, to reduce the social barrier of Bangkok elderly, elderly carpool support program by
neighborhood derivers was proposed. Based on the data collection in the area with poor access public
transport access, 35.92 and 48.54 percent of elderly were interested in using this carpool service on
weekday and weekend, respectively. On the other hand, 48.11 and 31.13 percent of neighborhoods were
interested to support elderly as carpool drivers on along their commuting route, and even non-
commuting route, respectively. This study also clarified the appropriate service price paid for
compensating extra travel cost of neighborhoods. By applying Kishi’s Logit PSM method, the amount
that elderlies were willing to pay was 24.55 JPY but the amount of service price that could persuade
iii
enough number of neighborhood to support those elderly was 28.00 JPY per kilometer. Thus, this
difference should be subsidized from the government.
Chapter 9 expresses the total conclusion of study result and summary, as well as the proposed
transportation policy implications for the reduction of social exclusion. This study provided the new
finding of clarifying the situation of social exclusion problem caused by transport difficulty of Bangkok
low income and elderly. While developing countries are heading towards mature societies, this study
would greatly contribute to the solution to combat social exclusion by transport policy in the future.
iv
TABLE OF CONTENTS CHAPTER 1 INTRODUCTION ............................................................................................................ 1
1.1 Background ...................................................................................................................... 1
1.2 Statement of Problem ....................................................................................................... 1
1.2.1 Transport difficulty of Bangkok low-income people associated to social exclusion 2
1.2.2 Transport difficulty of Bangkok elderly people-related social exclusion ................. 3
1.3 Research Objective .......................................................................................................... 4
1.4 Dissertation Overview ..................................................................................................... 4
CHAPTER 2 WHAT IS SOCIAL EXCLUSION ................................................................................... 6
2.1 The General Concept of Social Exclusion ....................................................................... 6
2.2 Terminology ..................................................................................................................... 6
2.2.1 Social exclusion ........................................................................................................ 6
2.2.2 Social segregation ..................................................................................................... 7
2.2.3 Social inclusion ......................................................................................................... 7
2.2.4 Social integration ...................................................................................................... 7
2.3 Dimension of Social Exclusion ........................................................................................ 8
2.4 Causes of Social Exclusion .............................................................................................. 9
2.5 Transport Disadvantage Related Social Exclusion ........................................................ 10
2.6 The Measurement of Social Exclusion .......................................................................... 10
2.7 Progress in Policy and with Practical Delivery.............................................................. 11
2.7.1 Experiences of the UK ............................................................................................ 13
2.7.2 Experiences of Australia ......................................................................................... 13
2.8 Category of Policy to Combat Social Exclusion............................................................ 14
2.8.1 Types of policies ..................................................................................................... 14
2.8.2 Multidisciplinary policies ....................................................................................... 15
CHAPTER 3 LITERATURE REVIEW ............................................................................................... 17
3.1 Transport-Related Social Exclusion .............................................................................. 17
3.2 Study Areas and the Index to Measure the Degree of Social Exclusion ....................... 19
3.3 Transport Difficulty and Social Exclusion of Low Income ........................................... 20
3.3.1 Transport difficulty of low-income group leading social exclusion ....................... 20
3.3.2 The previous transport subsidy policy to support low income for transportation .. 21
3.3.3 The distribution of transport subsidy policy to target group................................... 22
3.4 Transport Difficulty and Social Exclusion of Elderly ................................................... 23
3.4.1 Transport difficulty of elderly leading social exclusion ......................................... 23
3.4.2 Ride sharing program for supporting travel needs of elderly living in the area with
poor transportation access ................................................................................................ 24
3.5 The Concept Hard and Soft Infrastructure Policies ....................................................... 24
v
3.6 Literature-Related Concepts of Statistical Model Used in The Study ........................... 25
3.6.1 Binary logit model .................................................................................................. 25
3.6.2 Ordered logit model ................................................................................................ 26
3.6.3 Count data regression analysis ................................................................................ 26
3.6.4 The best fit of model for discrete choice model ..................................................... 27
3.7 Structural Equation Model (SEM) ................................................................................. 28
3.7.1 The concept of SEM ............................................................................................... 28
3.7.2 Assessing Goodness-of-fit of SEM ......................................................................... 29
3.7.3 The statistical software used for the analysis of SEM ............................................ 29
3.8 Kishi’s Logit PSM (KLP) .............................................................................................. 30
3.8.1 The development of KLP ........................................................................................ 30
3.8.2 The usability of KLP ............................................................................................... 32
3.8.3 The application of KLP in the previous researches ................................................ 33
3.9 Intercepted Interview Approach .................................................................................... 33
3.10 Summary of Literature Review .................................................................................... 34
CHAPTER 4 CURRENT SITUATION OF TRANSPORTATION IN BANGKOK ........................... 35
4.1 Outline of Bangkok ........................................................................................................ 35
4.2 Transportation Systems of Bangkok .............................................................................. 36
4.2.1 Road system ............................................................................................................ 37
4.2.2 Public transportation system ................................................................................... 38
4.3 Current Transport Policies to Support General Low-Mobility Group in Bangkok ....... 44
4.4 Transport Difficulty and Social Exclusion of Low Income in Bangkok ....................... 44
4.4.1 The success in poverty reduction in Thailand ......................................................... 44
4.4.2 The challenge of increasing transportation affordable for low-income group in
Bangkok ........................................................................................................................... 45
4.4.3 Public transport subsidy in Thailand to support low-income in Bangkok .............. 45
4.5 Transport Difficulty and Social Exclusion of Elderly in Bangkok ................................ 48
4.5.1 The awareness of changing in Thai population structure ....................................... 48
4.5.2 Aging society and transport difficulty in Bangkok ................................................. 49
4.5.3 Transportation policy to support elderly people in Bangkok .................................. 51
CHAPTER 5 FRAMEWORK OF THE STUDY ................................................................................. 52
5.1 Framework of the Study................................................................................................. 52
5.2 Study Design .................................................................................................................. 53
5.2.1 The definition of social exclusion in this study ...................................................... 53
5.2.2 The originality of the study ..................................................................................... 53
5.2.3 The reason why low-income and elderly group were focused ............................... 54
5.2.3 The design of discussion of policy implication base on the overall result of the
study ................................................................................................................................. 56
vi
CHAPTER 6 PUBLIC TRANSPORT SUBSIDY TO REDUCE SOCIAL EXCLUSION OF
BANGKOK LOW INCOME ................................................................................................................ 57
6.1 Methodology .................................................................................................................. 57
6.1.1 Data collection and the measurement of variables ................................................. 57
6.1.2 To investigate the relationship between reduction of travel cost and increased trip
frequency.......................................................................................................................... 58
6.1.3 To estimate the error of inclusion of the subsidy .................................................... 59
6.2 Data ................................................................................................................................ 60
6.2.1 Descriptive statistic ................................................................................................. 60
6.2.2 Increase in trip frequency and decrease in degree of feeling social exclusion ....... 60
6.2.3 Unintended benefit .................................................................................................. 63
6.3 Statistical Analysis ......................................................................................................... 64
6.4 Discussion ...................................................................................................................... 67
6.4.1 Influenced factors from the model .......................................................................... 67
6.4.2 To improve free train policy ................................................................................... 68
CHAPTER 7 THE INVESTIGATION OF SOCIAL EXCLUSION CAUSED BY CURRENT
TRANSPORT DIFFICULTY OF BANGKOK ELDERLY ................................................................. 69
7.1 Methodology .................................................................................................................. 69
7.1.1 Data collection and the measurement of variables ................................................. 69
7.1.2 To examine the relationship between satisfactory with transportation, socio
demographic and gaps in number of trips (model 1) ....................................................... 71
7.1.3 To investigate the relationship between gaps in number of trips and feeling of
social exclusion (model 2) ............................................................................................... 72
7.1.4 To recommend how to increase the satisfaction degree of transportation services
(model 3) .......................................................................................................................... 73
7.1.5 To examine all relationships simultaneously by structural equation model (SEM)74
7.2 The Result Analyzed by Regression Methods ............................................................... 75
7.2.1 Socio-economic characteristics ............................................................................... 75
7.2.2 Trip purposes .......................................................................................................... 77
7.2.3 Daily transportation and gaps in number of trips.................................................... 78
7.2.4 Measurement of the degree of social exclusion ...................................................... 81
7.2.5 The approach to improve the degree of satisfaction with transportation ................ 83
7.3 The Result Analyzed by SEM Approach ....................................................................... 86
7.3.1 Socio-economic characteristics of the samples used for the analysis of SEM ....... 86
7.3.2 Degrees of satisfaction with transportation of the SEM samples ........................... 87
7.3.3 Desired levels of social participation of the SEM samples..................................... 89
7.3.4 Degree of social exclusion of the SEM samples ..................................................... 90
7.3.5 Structural equation modelling for the process of social exclusion ......................... 91
7.3.6 The process for feelings of social exclusion in elderly private car users ................ 94
vii
7.3.7 The process for feelings of social exclusion in elderly public transport users ....... 94
7.3.8 The approach to reduce the feelings of social exclusion ........................................ 95
CHAPTER 8 ELDERLY CARPOOL SUPPORT PROGRAM BY NEIGHBORHOOD DRIVERS IN
THE AREA WITH POOR ACCESSIBLE TRANSPORTATION ...................................................... 97
8.1 Methodology .................................................................................................................. 97
8.1.1 Data collection ........................................................................................................ 97
8.1.2 To investigate which factor affecting the decision of elderly to use the carpool
service, and the decision of Bangkok residents to support elderly .................................. 99
8.1.3 To examine the amount of willingness to pay by KLP method .............................. 99
8.1.4 To define the appropriate service price that could cover whole demand of elderly
people ............................................................................................................................. 100
8.2 Data and Results .......................................................................................................... 101
8.2.1 Descriptive statistic ............................................................................................... 101
8.2.2 The willingness to join the elderly carpool service program ................................ 103
8.2.3 The amount of willingness to support elderly of residents ................................... 106
8.2.4 The service price perception of elderly for using carpool service ........................ 106
8.2.5 Pricing and policy implication .............................................................................. 107
8.2.6 The improvement of trip frequency and the reduction of feelings of social
exclusion ........................................................................................................................ 108
CHAPTER 9 CONCLUSION AND RECCOMENDATION............................................................. 110
9.1 Summary ...................................................................................................................... 110
9.1.1 Public transport subsidy to reduce social exclusion of Bangkok low income ...... 110
9.1.2 The investigation of social exclusion caused by current transportation difficulty of
Bangkok elderly ............................................................................................................. 111
9.1.3 Elderly carpool support program by neighborhood drivers in the area with poor
accessible transportation ................................................................................................ 112
9.2 Policy Implication ........................................................................................................ 114
9.2.1 Policy implication from chapter 6 for Bangkok low income ................................ 114
9.2.2 Policy implication from chapter 7 for Bangkok elderly ....................................... 114
9.2.3 Policy implication from chapter 8 for Bangkok elderly living the area with poor
accessible transportation services .................................................................................. 115
9.3 Research Contribution ................................................................................................. 115
9.4 Limitation and Recommendation ................................................................................. 116
BIBLIOGRAPHIES ............................................................................................................................ 117
APPENDIX A ..................................................................................................................................... 127
APPENDIX B-1 .................................................................................................................................. 135
APPENDIX B-2 .................................................................................................................................. 144
APPENDIX C ..................................................................................................................................... 153
viii
LIST OF FIGURES
Figure 1-1: Thai aged population forecasting (Aging Society in Thailand, 2016) ................................. 3
Figure 1-2: The framework of the study ................................................................................................. 4
Figure 2-1: The difference between exclusion, segregation, inclusion and integration .......................... 8
Figure 2-2: Diagram to illustrate relationship between transport disadvantage, social disadvantage and
social exclusion (Lucas, 2012) .............................................................................................................. 11
Figure 3-1: The illustration of the errors of inclusion and exclusion of subsidy policy (Foster and
Araujo, 2004) ........................................................................................................................................ 23
Figure 3-2: The logistic function .......................................................................................................... 26
Figure 3-3: The structure of multiple ordered choices .......................................................................... 26
Figure 3-4: The analysis techniques used in SEM ................................................................................ 28
Figure 3-5: The analysis techniques used in SEM ................................................................................ 28
Figure 3-6: Price Sensibility and Willingness to Buy ........................................................................... 30
Figure 3-7: The price indicator references of PSM and KLP (Kishi and Sato, 2005) .......................... 31
Figure 3-8 Estimation of Market Size by KLP ..................................................................................... 32
Figure 4-1: Map of Bangkok ................................................................................................................. 35
Figure 4-2: Land use of Bangkok ......................................................................................................... 36
Figure 4-3: Transportation mode share of Bangkok ............................................................................. 36
Figure 4-4: Road system of Bangkok ................................................................................................... 37
Figure 4-5: Number of register vehicle in Bangkok ............................................................................. 37
Figure 4-6: Proportion of registered vehicle of Bangkok in 2018 ........................................................ 38
Figure 4-7: Buses types ......................................................................................................................... 39
Figure 4-8: Major bus route of Bangkok .............................................................................................. 39
Figure 4-9: The previous BRT route in Bangkok ................................................................................. 40
Figure 4-10: Bangkok transit ridership per year ................................................................................... 40
Figure 4-11: Bangkok transit ridership per year ................................................................................... 41
Figure 4-12: Metro routes of Bangkok ................................................................................................. 41
Figure 4-13: Bangkok metro ridership per year .................................................................................... 41
Figure 4-14: Public train ....................................................................................................................... 42
Figure 4-15: Public train map ............................................................................................................... 42
Figure 4-16: Public train ridership ........................................................................................................ 42
Figure 4-17: Bangkok public boat ........................................................................................................ 43
Figure 4-18: Public boat station of Bangkok ........................................................................................ 43
Taxi Tuk tuk Motorcycle taxi .................................. 44
Figure 4-19: Bangkok paratransit vehicle ............................................................................................. 44
Figure 4-20: Rail system of Thailand ................................................................................................... 46
Figure 4-21: Demographic situation ..................................................................................................... 50
Figure 5-1: The framework of the study ............................................................................................... 52
Figure 6-1: Survey locations ................................................................................................................. 58
Figure 6-2: The structure of the analyses .............................................................................................. 59
ix
Figure 6-3: The number of free train users travelling more frequently ................................................ 62
Figure 6-4: Increase of trip frequencies ................................................................................................ 63
Figure 6-5: Decrease in degree of social ............................................................................................... 63
Figure 7-1: The survey location ............................................................................................................ 70
Figure 7-2: The conceptual diagram of thresholds in ordered logit model when the number of
categories can be varied by the number of range of dependent variable .............................................. 73
Figure 7-3: The structure of the analyses .............................................................................................. 75
Figure 7-4: The structure of SEM ......................................................................................................... 75
Figure 7-5: Degree of importance of activities and gap in desired number of trips by activity............ 77
Figure 7-6: Mode uses and gaps of the numbers of trip by mode ......................................................... 78
Figure 7-7: Differences of the feelings of social exclusion between the situation of existing and
desired trip frequencies ......................................................................................................................... 82
Figure 7-8: The simulation of psychological scores in five dimensions categorized by modes ........... 84
Figure 7-9. Degree of satisfaction with daily transportation of the SEM samples ............................... 88
Figure 7-10: Gaps in existing and desired number of trips by mode of the SEM samples ................... 89
Figure 7-11: Degrees of social exclusion in five dimensions of the SEM samples .............................. 90
Figure 7-12: Standardized SEM for the Process of Social Exclusion in Elderly Private Car Users ..... 93
Figure 7-13: Standardized SEM for the Process of Social Exclusion in Elderly Public Transport Users
.............................................................................................................................................................. 93
Figure 8-1: Survey locations ................................................................................................................. 98
Figure 8-2: The price indicator references of KLP (Kishi and Sato, 2005) ........................................ 100
Figure 8-3: The structure of the analyses ............................................................................................ 101
Figure 8-4: Origins (blue locations) and destinations (red locations) of trips of respondents ............ 103
Figure 8-5: The amount of willingness to support elderly in carpool service of Bangkok resident ... 104
Figure 8-6: Price indicators analyzed by KLP method ....................................................................... 107
Figure 8-7: Trip frequency and degree of feeling social exclusion at current situation and hypostatical
situation (when carpool service is available) of elderly respondents .................................................. 108
x
LIST OF TABLES
Table 2-1: Seven dimension of social exclusion proposed by Church et al. (2000) ............................... 9
Table 2-2: Four keys dimension of social exclusion by London School of Economic ........................... 9
Table 2-3: The methods used in previous studies to measure the degree of social exclusion .............. 12
Table 3-1: Summary of the examples of previous research of transport-related social inclusion ........ 18
Table 3-2: Literatures of how to measure the degree of social exclusion-related transportation ......... 20
Table 3-3: Summary of previous research related to public transport subsidy ..................................... 21
Table 4-1: Number of vehicle and service route of transit system of Bangkok .................................... 38
Table 4-2: Time series, number of users, and subsidy budgets of free train policy in Thailand .......... 47
Table 4-3: Time series, number of users, and subsidy budgets of free bus policy in Thailand ............ 48
Table 6-1: Descriptive statistics of frequency data (392 respondents) ................................................. 61
Table 6-2: Descriptive statistics of continuous data ............................................................................. 62
Table 6-3: Increase of trip frequencies ................................................................................................. 62
Table 6-4: Descriptions of variables ..................................................................................................... 65
Table 6-5: Estimated parameters of the Binary Logit Model (BLM) ................................................... 66
Table 7-1: Socio economic information. ............................................................................................... 76
Table 7-2: Factor analysis of degree of satisfaction with transportation .............................................. 80
Table 7-3: Count data regression analyses of gaps in the numbers of trips categorized by mode ........ 81
Table 7-4: Ordered logit models of the psychological scores ............................................................... 83
Table 7-5: Ordered logit models of degrees of satisfaction with transportation ................................... 85
Table 7-6: Socio-economic information of the data used for analysing SEM ...................................... 87
Table 8-1: Descriptive statistic of Bangkok 103 elderly and 106 residents ........................................ 102
Table 8-2: The number of respondents willing to join the carpool support program ......................... 104
Table 8-3: The binary logit model of the factor affecting the decision to join the carpool program .. 105
Table 8-4: Simulations of the supply-demand ratio at price indicators analyzed by KLP and the trial
price where S/D is equal to 1 .............................................................................................................. 108
Table 8-5: Mean comparisons analysed by statistical T-test of trip frequency and degree of feelings
social exclusion ................................................................................................................................... 109
1
CHAPTER 1 INTRODUCTION
1.1 Background
Transportation disadvantage is a factor that can lead to the problem of social exclusion in the society.
Transport difficulty can cause the inaccessibility to social services, activities and opportunity of the
community. In general, transport disadvantage (e.g. high cost of fare, poor services and inconvenience)
and social disadvantage (e.g. ill-health condition, unemployment and low-income) are intercepted,
causing transport shortage that could result in unreachability to activity places of community, leading
to the feeling social exclusion (Currie and Delbosc, 2010a; Lucas, 2012). Recently, transport
difficulty-related social exclusion has become the national crucial issue among policy maker and
academic researcher in many countries in global.
The process of social exclusion can be described as a person’s inability to reasonably access or
participate in mainstream social activities and opportunities because of reduced accessibility and
inadequate mobility (Kenyon et al., 2003; Lucas, 2012). Social exclusion can be categorized into
multiple dimensions, such as psychological, sociological, political, economic and educational
(Burchardt, 2000; Church et al., 2000; Silver, 1994). A person can have more than one dimension of
social exclusion. For example, a person with transport difficulty cannot access both social service and
activity place in the city. Actually, the feeling of social exclusion is not a static state; instead, people
can move in and out of it, depending on the how change in their living condition (Atkinson and Hills,
1998).
It has been found that low-mobility group, such as elderly, disability and low-income people, tended to
have limited access to transportation ability and often relegated from the main stream of social activity
and opportunity of the community. Actually, an individual can possibly become more than a dimension
of low mobility, such as a person with both low income and disadvantage (Jansuwan et al., 2013). This
low-mobility population are risk to have lack of degree of social participation due to the less frequency
of trip to join the community activities, which can lead to the feeling of social exclusion (Wong et al.,
2018). Although there is broad awareness in developed countries of social exclusion caused by
transportation difficulty of these low-mobility group, this issue has received little attention in
developing countries, such as Thailand.
1.2 Statement of Problem
In the case of Bangkok, the capital city of Thailand, there are still various kinds of low-mobility
population, such as older adults, disadvantage person and people living far away from the public
transport station. According to the previous studies, because of transportation systems (e.g. bus, metro
and para-transit) in Bangkok have not been fully developed yet, the convenient level of the use of them
have not been satisfied by many groups of Bangkok population (Srichuae et al., 2016; Suparb and
Ranjith, 2009). It may become the major obstacle particular for low-income persons to go out for
participating in social activities outside their home (e.g. visiting friend or relative and hobby), as well
as for accessing to social service and opportunities (e.g. going to hospital and shopping places).
2
In current situation, Bangkok low-mobility populations have a variety of transportation needs, including
access to mandatory activities (e.g. working, employment, medical visits) and non-mandatory activities
(e.g. social outlets, recreation, and hobby). They also have various levels of individual support requests.
Even it is relatively difficult, some low-mobility persons are still able to travel by themselves, such as
driving their own private vehicles and using public transport services or without the support from
assistance. However, some of them need assistance from others to support them for travel needs. For
example, persons with disability needs personal assistances (e.g. friends, neighbor or family members)
to drive them to the destination where they want to go.
In the fact of the current situation in Bangkok, the problem of transport difficulty of low mobility group
has been already generally received high attention from the government. Both Bangkok government
and private sector have created various transport policies to encourage them to be able to go out to join
the community activities and access social opportunities. For example, the government has provided
and developed special transport facilities to support disadvantage persons for transportation needs, such
as the development of walkway for blind person, low-floor bus for wheelchair and transport assistance
service. Another example, door-to-door paratransit service was provided for young children to support
them to access social facility, such as school (Bangkok Enterprise, 2018; ThisAble, 2018). However,
the evaluation of transportation policy in terms of the reduction of the feeling of social exclusion of
Bangkok low-income and elderly has received only less attention from the policy maker.
1.2.1 Transport difficulty of Bangkok low-income people associated to social exclusion
Although Thailand has been one of the broadly mentioned as an example development achievement,
with the outstanding reduction of poverty, especially from 1980s. However, since 1997, domestic and
international investments of Thailand have been decelerated down because of the global recession of
economic. It resulted in a massive effect to the economic development in Thailand. Although the
poverty problem was relatively successfully solved, 8.6 percent of 69.0 million of Thai populations was
still grouped low income people with monthly income less than 22292 JPY in 2017 (Asian
Development Bank, 2017; Thansettakij, 2017; Ministry of Labour, 2014).
Generally, public transportation plays an important role to generate social activities, but the
affordability to transportation was dominated mainly by income of users (Horowitz, 1993). Since the
fare of public transportation in Thailand has been increasing year by year (Ministry of Transport,
2014), non-low-income can still afford for transportation and may be able to make more trips to go out
to join the society; on the other hand, the unaffordability of low-income group may limit their trip
frequency. As a result, with less frequency of travel, low-income group tended to participate in less
social activities, which consequently may lead to the feeling of social exclusion. The situation of social
exclusion caused by unaffordable transportation of low income has not been evaluated yet in Bangkok.
The major mobility obstacle of low-income group is unaffordability to transportation, so a possible
approach is to make them more affordable, such as public transport subsidy program. Although many
previous studies evaluated the effect of public transport subsidy on the increase the demand and trip
frequency of those users (e.g. Goeverden et al., 2006; Moreno-Monroy and Posada, 2018; Witte et
al., 2006), its effect on reduction of social exclusion was rarely focused, especially in Thailand.
3
1.2.2 Transport difficulty of Bangkok elderly people-related social exclusion
Aging populations have become a global phenomenon in not only developed countries but also
developing countries. From 2006 through 2050, there will be a dramatic growth in the number of global
population aged 60 years old and over, from 11% to 22% (World Health Organization, 2007). In
global south countries, such as Thailand, the proportion of the aging population will also see rapid
increases from 16% in 2010 to 30% in 2035 (Knodel et al., 2015). This is also apparent for Bangkok,
Thailand’s capital city as illustrated in Figure 1-1 (Suwanrada, 2014).
However, transportation (both driving and using public transportation) is still a common daily challenge
for the elderly due to their mental, physical, and financial conditions (DK Publishing, 2013; Wong et
al., 2018). Such rapid development in the population has occurred without sufficient infrastructure,
appropriate urban planning or adequate public transportation (Srichuae et al., 2016; Suparb and
Ranjith, 2009). As a result of this fact, people in Bangkok tend to travel by private vehicle because it
offers more convenience (Kaewwongwattana et al., 2016). Then again, driving is often more difficult
for the elderly than younger people due to their age-related physical characteristics. Further, travelling
by public transportation might be inconvenient for elderly people in various respects, such as
convenience, ability to gain the service information and safety. According to the previous studies, both
elderly drivers and public transport users in Bangkok are more likely to experience difficulties in
transportation.
Although there are many kinds of aspects of transportation service performances, which transport
performance is difficult for elderly and cause the feeling social exclusion to them has been rarely
investigated before. The degree of satisfaction with daily transportation of elderly people living in
Bangkok is still not investigated yet. Furthermore, it has not been clarified that unsatisfactory
transportation systems could lead to the feeling of social exclusion of elderly by discouraging elderly
people from going out to engage in social activities and services.
Figure 1-1: Thai aged population forecasting (Aging Society in Thailand, 2016)
4
1.3 Research Objective
According to the statement of problem, the goal of this study is to clarify the current situation of social
exclusion of caused by transportation difficulty of Bangkok low income and elderly, as well as to
propose transport policy implication to reduce their feelings social exclusion. The study focused on the
evaluation of the effect of transportation policy on the reduction of social exclusion, as well as proposing
transport policy which has been rarely focused in the low mobility transport support transport plan of
the Government. The objective of the study is written below.
• To measure the degree of social exclusion caused by transportation difficulty of Bangkok low
income and elderly groups
• To propose and evaluate transportation policy in terms of the reduction of social exclusion
1.4 Dissertation Overview
The desegregation is organized into seven chapters including biographies and appendices. The overall
dissertation flow is shown in Figure 1-2. The contents of each chapter are explained as below.
Figure 1-2: The framework of the study
5
• Chapter 1 presents the general background of transportation difficulty-related social exclusion
of low-mobility group, statement of problem, research rational and objective, scope of work,
and dissertation overview.
• In chapter 2, the concept, dimension and causes of social exclusion, often confused
terminologies, transportation associated with social exclusion, how to measure the degree of
social exclusion, and the previous experience of progress in policy combating social exclusion
are explained.
• For chapter 3, the literature associated with social exclusion caused by transportation difficulty,
the previous studies of transportation policy to reduce social exclusion of low mobility group,
and the content of statistical analysis methods used in the study are reviewed.
• Chapter 4 explains the outline of Bangkok, transportation systems, current transportation
difficulty-related social exclusion of Bangkok low mobility people and recent policies from the
government to support those people.
• Chapter 5, framework of the study, describes the overall framework of the study, how to design
the study flow, and the reason why low-income and elderly group were selected as focused
group in this study.
• Chapter 6 shows the methodology, data, outcome and argument of the impact of public
transport subsidy on social inclusion of low-income groups.
• In chapter 7, the methodology, data, result and discussion of transport difficulty-related the
feeling of social exclusion of Bangkok elderly, as well as policy implication are described.
• Chapter 8 expresses the methodology and result of the willingness to join the volunteer elderly
carpool support program, as well as the guidance of how to set the appropriate service price.
• In chapter 9, the summary of the study, policy implication, research contribution,
recommendation and further study are discussed.
6
CHAPTER 2 WHAT IS SOCIAL EXCLUSION
2.1 The General Concept of Social Exclusion
The notion of social exclusion was started from the concept of poverty. Poverty refers to persons who
cannot access to the basic needs. Poverty could be permanent or temporary (Ridge, 2002). Nevertheless,
the concept of social exclusion is broader than poverty. It refers to deny social and community activities.
The causes of social exclusion might include low-income, poor skills, unemployment and disadvantage.
There was no solitary fixed explanation of the terms poverty and social exclusion. These terminologies
are often used interchangeably. Therefore, it is necessary to differentiate between these two terms to
avoid the ambitious definition (Burchardt, 2000; Church et al., 2000).
• ‘Poverty’ refers to a relatively total access to material welfare
• ‘Social exclusion’ refers to a winder concept that some persons or families are not only poor,
but they also have furthermore feeling socially excluded by losing their ability to access with
social activities and opportunities that they need or would like to participate, such as
employments, social services, and facilities because of the reasons beyond his or her control.
Social exclusion was initially introduced in France. This term has been a term widely used in Europe.
It is used in multidiscipline including sociology, education, politics, economics and psychology (Silver,
1994). It was mentioned that social exclusion is not an exactly static state but it is a dynamic status that
persons are able to repeatedly move in and out of this condition (Atkinson and Hills, 1998). Social
exclusion can relegate residents to the fringe of community, resulting in the loss of opportunities and
social participation, as well as the human resource in society. Subsequently, social exclusion has
become a nationwide critical issue in the global (Hine and Mitchell, 2017).
2.2 Terminology
The concept of social exclusion is sometimes misunderstood with the definition of social segregation.
In addition, the description between social inclusion and social integration are also different and cannot
be used interchangeably. Therefore, in order to understand about the actually concepts of those terms,
it is important to distinguish those terms as describe below.
2.2.1 Social exclusion
Social exclusion refers to the process that people in the community are blocked from participating in
fundamental social services, opportunities, resources and rights which are generally available to all
residents, and which are important for social integration (as mentioned above) as shown in Figure 2-1
(a) (Wang, 2014).
7
2.2.2 Social segregation
Social segregation refers to separation between two or more population groups by class, race or ethnic
group in the society, and it is also related with spatial and residential segregation in a restricted area as
shown in Figure 2-1 (b) (Massey and Denton, 1988). Socio-spatial segregation is one of the most
widely used in the field of urban and social geography. The spatial mismatch barriers segregated group
to social connection, resulting in the unequal social facility, opportunity and service provisions, leading
to inequity of the accessibility to the basic needs, such as educational accomplishment (Card and
Rothstein, 2007), employment opportunities (Massey et al., 1987), and health service (Williams and
Collins, 2001). However, it was found that some segregated groups were willing to live separately and
denies other groups from access to the community. This situation might lead them to the isolation,
violence, deprivation and exclusion (Massey and Denton, 1988).
2.2.3 Social inclusion
Social inclusion refers to the term used to explain the measures to attain the equality of access to services
and opportunities in community. It encourages residents to join their community and society and
supports the involvement of people to cultural and social activities as shown in Figure 2-1 (c) (Silver,
2016). The aim of social inclusion is to encourage marginalized people (e.g. poor and disadvantage
groups) to take part in their rights, social activities, services, and opportunities, and to be aware of social
discrimination. In addition, social inclusion aims to guarantee that residents can have their voice in any
decisions which affect their living. Therefore, all people can have an equal right to access to services
and market, social, political and spaces. In addition, social inclusion defines how a society respects their
differences, values all citizens, ensures the access to fundamental requirements, and enables full social
participation. For example, disability person can have the same accesses, choices and rights as
everybody else in the city. Social inclusion has been considered as universal human right (Wang, 2014).
2.2.4 Social integration
Social integration refers to the corporation of different persons or groups in the society as equals as
shown in Figure 2-1 (d). For example, new arrival group experiences cooperative social interaction
with their group members, attraction to the group and fulfillment with other groups (Wang, 2014).
Social integration is also defined as the new arrival persons emerging a social interaction and being
accepted and linked by local people (Bauer and Green, 1998; Morrison, 2002). As a result of access
to people and network, social integration allows newcomers to have social resources and capital
whenever they need support. In addition, it maintains peaceful social relations and fosters the stable
societies based on the protection of human rights, respect the diversity and non-discrimination. Social
integration can be structured and dynamic procedure which can be move in and out and social
integration does not happen by forced corporation (Ashford and Black, 1996).
8
Figure 2-1: The difference between exclusion, segregation, inclusion and integration
2.3 Dimension of Social Exclusion
There are several studies described the dimension of social exclusion. In terms of social participation,
general social activities can be categorized into three groups, including the following (De Sousa et al.,
2014).
1. Anchoring activities, or activities on a fixed schedule that change little, such as working and
duty
2. Mandatory activities, or activities required to satisfy basic human needs such as daily shopping,
whose date and time can be adapted
3. Non-mandatory activities, or those that are engaged in to satisfy a person’s desires, such as
leisure activities, for which the time and place are more flexible
The dimensions of social exclusion differ by population group. Whereas the social exclusion of younger
people is commonly evaluated using education and family support, the social exclusion of working-age
people generally focuses on employment and income level (Aldridge et al., 2011; Department of
Social Security, 1998). However, the dimensions of the social exclusion of older people (generally,
those who are retired) highlight such people’s level of access to basic needs, degree of social
participation, and transportation ability (Jansuwan et al., 2013; De Sousa et al., 2014). (Church et al.
(2000) proposed seven dimensions of social exclusion as shown in Table 2-1 and (Saunders, 2003)
proposed four keys dimension social exclusion proposed at London School of Economic (LSE) as
shown in Table 2-2.
9
Table 2-1: Seven dimension of social exclusion proposed by Church et al. (2000)
Seven dimensions of
social exclusion Example
1. Physical exclusion
- Older adult, young children, person with disadvantage, persons
without local language skill
- Inaccessibility to transportation services
2. Geographical exclusion - Segregation
- Inadequate transportation provision
3. Exclusion from facilities - Inaccessible to education, financial, leisure, health facilities and
shopping place
4. Economic exclusion - Income restraints and limited access of transportation to access
market information and job location
5. Time-based exclusion - The difficulties of managing time
- Time poverty
6. Fear-based exclusion - Fear of crime
- Anxiety to society
7. Space exclusion - Problem of the provision of public space, such as accessibility,
uncomfortable or unsafety
Table 2-2: Four keys dimension of social exclusion by London School of Economic
Four keys dimension
of social exclusion Example
1.Consumption Where total net income of household is less than half mean income
2.Production The person who is un employed and no education or training
3.Political engagement The person who does not engage or vote in the general election
4.Social interaction
(phycology)
The person who lacks somebody who will offer relax with, appreciation,
comfort, support in emergency, and listening
2.4 Causes of Social Exclusion
Previous studies sough to find crucial factors relating to the process of social exclusion. socio-
demographic factors such as gender, age, educational level, employment status and income affected the
feeling of social exclusion (Bonsall and Kelly, 2005; Lucas et al., 2011; Social Exclusion Unit, 2003).
The typical risk groups were from the marginal group of the society, such as elderly, low-income,
disability person and people without friend or family (Engels and Liu, 2011; Lucas et al., 2011;
Stanley et al., 2011). Psychologic status also affected the decision to join the community. For example,
some persons do not participate in any community activity because they have social anxiety, lack of
confidence or do not want to (Egger et al., 2003; Shergold and Parkhurst, 2012).
10
Site-level characteristic, such as living area, spatial factor and city planning also dominates how much
social services, activities and opportunities which citizens could participate in. For example, older
people living in the area where public transport services were difficult to be accessed tended to
participate in less social activities than those living near the public transport station (Shergold and
Parkhurst, 2012). In addition, the site economic factor such as, price of product and cost of living also
affected the frequency of going out for joining some social activities, such as shopping, hobby and
leisure (Rajé, 2007; Schönfelder and Axhausen, 2003).
The local social factors, such as culture, ethnicity and race also significantly dominates the life style of
residents in the community (Agulnik et al., 2002). The community with social discrimination is risk to
have the problem of social exclusion in relegated group, such as racist or sexism (Priya and Uteng,
2009; Room, 1998). Those people tended to have less chances or unequal right to access social services
or opportunities, such as job opportunity (Preston and Rajé, 2007). In addition, multi-language society
lead to the less social interaction among those groups using different languages, resulting in the higher
degree of social exclusion (Imrie and Imrie, 1996).
2.5 Transport Disadvantage Related Social Exclusion
The concept of the relationship between social exclusion and transport disadvantage has been
cumulatively researched since a few decades ago. Transport difficulty may lead residents to social
exclusion by barriers to exclusion from social services, employment, perceptions and fear of safety, and
medical service inequalities and decrease in educational achievements (Clifton and Lucas, 2004).
Transportation-related social exclusion has been widely known as theoretical concept for explaining
progress of social exclusion caused by transport difficulty.
Transport disadvantage is related with only parts of social exclusion. For the instance, it is possible that
a person is socially excluded but he/she still can access to transportation services, and vice versa (Currie
and Delbosc, 2010b). Transport disadvantage and social disadvantage are intercepted both indirectly
and directly, causing poverty of transportation, resulting inaccessibility, consequently followed by the
feeling of social exclusion as shown in Figure 2-2 (Lucas, 2012).
2.6 The Measurement of Social Exclusion
In academic research-related social exclusion, there are various methods proposed by researchers in
order to measure the feeling of social exclusion of the residents living in the community. The earlier
studies tended to interpret the degree of social exclusion categorized by the dimension of social
exclusion, such as the application of LSE model (e.g. Diener et al., 1985; Ryff, 1989; Watson et al.,
1988) and category approach (w.g. Askham and Warnes, 1992; Rajé, 2007). After that, this method
has been still used, such as the study of (e.g. Stanley and Vella-Brodrick (2009).
The measurement of the accessibility to the social activities and services is the broadly methods to
measure the degree of social exclusion, such as the application of spatial approach (e.g. Church et al.,
2000), Geographic Information System (GIS) approach (e.g. Matthews et al., 2003) and A
Methodology for Enhancing Life by Increasing Accessibility (AMELIA) (e.g. Mackett et al., 2008).
11
Figure 2-2: Diagram to illustrate relationship between transport disadvantage, social disadvantage and
social exclusion (Lucas, 2012)
Various studies degree the social exclusion by measuring the activity sizes or how much residents can
travel in community to participate in social activities, such as the studies of (Schönfelder and
Axhausen (2003) and (Paez et al. (2010). In addition, mobility level of people living in the city is also
the important indicate to represent the ability to participate in the society, such as the studies of (Church
et al. (2000), (Kaufmann et al. (2004) and (Priya and Uteng (2009).
Poverty line is also the tool to interpret the possibility of social exclusion. It was implied by previous
studies that persons with tended to have higher chance to be socially excluded (e.g. The Poverty Site,
2008; Unkles). Some studies measure the feeling exclusion by asking the residents directly to rate the
degree of their feeling social exclusion based on Linkert scale (e.g. Currie et al., 2010). Finally, the
methods to measure the degree of social exclusion are summarized in Table 2-3.
2.7 Progress in Policy and with Practical Delivery
After the transport disadvantage-related social exclusion became the nationwide crucial issue, the
governments of those countries sought to create the social policy to combat social exclusion. Based on
the literature, the earlier countries that established the organization and institute, as well as developed
the public policy and campaign to encourage social inclusion are the UK and Australia. This section
introduces the Progress in the development of policy of these two countries as shown below.
12
Table 2-3: The methods used in previous studies to measure the degree of social exclusion
Method Index Author
Interpretation by categorization
LSE model
(London School of
Economic Model)
The degree of production, political engagement,
social interaction, consumption
(Diener et al., 1985;
Ryff, 1989; Stanley
and Vella-Brodrick,
2009; Watson et al.,
1988)
Q methodology The coefficient of degree of social participation in
each social activity group (Rajé, 2007)
Category approach The degree of social participation in each social
activity group
(Askham and Warnes,
1992)
Measurement of accessibility
Spatial approach
The portion of households with in 400m and
800m from public transport stations and facilities
The access distance to social facilities
(Church et al., 2000)
Spatial approach The accessibility to social service, opportunity
and facility
(Özkazanç and
Özdemir Sönmez,
2017)
GIS approach The level of accessibility to the social service and
facility of wheelchair users
(Matthews et al.,
2003a)
AMELIA: A
Methodology for
Enhancing Life by
Increasing
Accessibility
The number of people in a particular group who
can reach the opportunities being considered as a
result of the implementation of the policy action
Cost effectiveness by comparing cost of project
per beneficial person who can access to social
policies
(Mackett et al., 2008)
Measurement of activity size
Measure of activity
space size by
spatial approach
Area of activities done by person
Frequency and distance travelled by residents
(Paez et al., 2010;
Schönfelder and
Axhausen, 2003)
Measurement of mobility
Spatial approach by
quantitative and
qualitative analysis
(i) Access: access range of available mobilities
according to time and place
(ii) Competence: the abilities related to the
appropriate access
(iii) Appropriation: how persons interpreted and
perceived their access competences
(Church et al., 2000;
Kaufmann et al.,
2004; Priya and
Uteng, 2009)
Measurement of poverty line
Interpretation of
income Income level comparing with the poverty line
(The Poverty Site,
2008; Unkles, 2008)
Rating the feeling by scale
Psychological
question
The score of feeling social exclusion based on
Linkert scale (Currie et al., 2010)
13
2.7.1 Experiences of the UK
Social Exclusion Unit (2003) and (Lucas, 2012) described the experiences of UK as below.
• 1990: From 1990s to 2000s, there was an increase in attention amongst UK academic researcher
and policy creators in the problem of transport disadvantage-related social exclusion. In
addition, it was related to a growing in awareness about the problem of social exclusion of low
income people living in the city.
• 1997: In December 1997, the UK Prime Minister established the Social Exclusion Unit (SEU).
• 2000: Social exclusion was significant topic in the academic sector in not only UK but also
Australia, Africa and the US
• 2002–2003: The study of transport-related social exclusion of Social Exclusion Unit (SEU) is
extensively known as a significant effect on policy maker.
• 2003: Social Exclusion Unit (SEU) played the importance role on influent policy and created
the accessibility planning in the UK.
• 2004: The UK Local Transport Plans (LTP) was established to clarify the relationship between
risk group of social exclusion and local transportation. During that periods, the Geographic
Information System (GIS) was enhanced and became the base tool for the spatial analysis.
• 2006: Since2006, The Five Year Local Transport Plans (LTP) had been created. It required the
local transport authorities to assess the accessibility and undertake strategic policy.
• 2007: Not only promoting social inclusion but the concept of well-being has been also become
the goal of policy maker.
• 2008: The UK government implemented the bus patronage program, but it was suspended
already.
• 2009: Some authorities targeted to improve the accessibility for the specifically socially
excluded persons. However, other group of authorities tried to propose more universal strategic
measures for improving the whole accessibility in UK.
• 2010: It was argued that it is possible to be socially excluded but still be able to access to
transportation services, or to be transport disadvantaged but still have highly socially included.
• 2012: To solve the problem of social exclusion, the concept of multidiscipline became the
important concept. It was argued that not only supporting social inclusion policy but also other
related policies such as education, economic, urban planning and health policy should be
integrated to combat social exclusion.
2.7.2 Experiences of Australia
The experiences of Australia based on the study of (Victoria, 2005), (Loader and Stanley, 2009),
(Lucas and Currie, 2011) and (Lucas, 2012) are shown below.
• 1992: Transport difficulty became the national issue of Australia
• 2004: Policy makers and transport academic researcher and in the Melbourne debated the
problem of transport-related social exclusion together in the Australian framework context.
• 2005: The policy plat form to combat social exclusion was created. A fairer Victoria policy
focuses on improving the accessibility to social services, enhancing social assistance for
disadvantaged persons, and decreasing barriers to social opportunities.
14
• 2007: More activities and policies to combat social exclusion were created, such as
- Transport and Social Inclusion Committee (TASIC) held the conference to propose
the campaign plan for the Australian government action to increase the feeling of
social inclusion from a transportation viewpoint
- Social transit, Victorian policy, including fixed route bus services, which focus on the
community that have no or limited transport options
- The Transport Connections Program (TCP) was implemented. This program offered
the financial support to hire local transportation coordinators to build the partnerships
between local transport planners and the residents in community to create the
appropriate community transportation plan. An around 4.19 million AU$, flexible
fund, was subsidized to support the program.
• 2008: The government established Victoria Department of Transport (VICDOT) to lead the
progress of a transport and social exclusion agenda through the conferences and seminars
• 2009: The Smart bus program to enhance social transit services in Outer Melbourne was
implemented. This program was subsidized by the flexible transport fund. This policy led to
both social inclusion and patronage development.
• 2010: Victoria Department of Transport (VICDOT) established the Social Transit Unit (STU)
to corporately work with other departments and the community sectors to clarify the problem
or causes that prevent residents from accessing the public transportation service.
2.8 Category of Policy to Combat Social Exclusion
The policies to combat social exclusion, created by the governments and policy makers, can be
categorized in to various types. In order to understand the difference in types of policies, this section
introduces the how to categorize the policy and the example of policy to combat social exclusion as
described below. Stewart et al. (2005), Stewart et al. (2008) and Kobler (2015) proposed four physical
types of public policy as below.
2.8.1 Types of policies
• Direct policy (target policies): Direct policy directly reaches the disadvantaged and targeted
groups. For example, specific welfare for low-income group. Errors of omission of direct policy
may be high. It can be effective but may stimulate the feeling inequity of non-target group.
Direct policies principally suitable when the disadvantaged group is well defined and socially
excluded group is small, comparing with total population.
• Indirect policies (universal policies): Indirect policies is applicably distributed to whole
population, planned to contribute to reducing the social exclusion for a whole. For example, a
universal health services, universal citizenship for all residents, guarantee of full employment,
progressive taxation and anti-discrimination laws. However, indirect policy may take time and
not always effective, or fail to reach the most excluded group.
• Pre-distribution policies: It is the concept of aiming to prevent inequalities before occurring,
for example, strengthening trade unions to improve wages and minimum or maximum wage
policy rather than using tax-and-spend redistribution to tackle inequalities after they have
occurred, as well as education policies and competition laws.
15
• Redistribution policies: It changes the distribution after the occurring of social exclusion, for
example, the policy to encourage those disadvantaged groups to come back and join the
community again after they were socially excluded.
2.8.2 Multidisciplinary policies
Not only one policy from a single discipline can effectively reduce the problem of social exclusion but
also other policies from other disciplines should be implemented together to reach the most
effectiveness of preventing social exclusion. For example, to promote social inclusion for elderly
people, the campaign of encourage elderly people to participate in community is needed, but other
policies, such as the improving of accessibility to the activity place and activity space management are
also necessary. The examples of previous policies to combat social exclusion categorized by sector and
discipline are written below.
1. Examples of the application of law and regulation
• People with mobility restrictions have their needs met when laws are fulfilled,
decrees and standards developed for disabled people who earn more protection by
demanding their rights with Laws No. 10,048 (Miranda et al., 2014)
• The law of design criteria, such as The Brazilian technical, NBR 9050 (Mackett et
al., 2008)
2. Examples of the application of social and cultural campaign
• Promoting citizenship and enabling individuals to have access to public policy
making arenas (Burchardt, 2000)
• Encouraging social equity cohesion, or solidary, promoting human right such as
disability, young, elderly or disadvantage persons and social justice principles (Sen,
1987)
3. Examples of the application of national plan
• Master Plan for Urban Development that a part of the plan focuses on social network
and interaction (PDDU) in Brazil (Miranda et al., 2014).
4. Examples of the application of local plan
• Single Regeneration Budget (SRB) in UK (Hodgson and Turner, 2003)
• Mersey travel’s Community Links Strategy in North West UK (Lucas, 2012)
• Cooperation among organization (Social Exclusion Unit, 2003) such as,
- Social Exclusion Unit in UK
- Transport and Social Inclusion Committee (TASIC) in AUS
- Victoria Department of Transport (VICDOT)
- Social Transit Unit in Victoria
- Asian development Bank (ADB)
- World Bank
• Redistribution of transport wealth in the interests of fairness or justice (Lucas, 2004)
16
5. Examples of general public policy
• Universal health insurance policy (Stewart et al., 2005)
• Information, advertisement and communication of public services and facilities
(Banister, 2008)
• Universal Coverage Scheme (UCS) in Thailand, 2001 (Tangcharoensathien et al.,
2015)
6. Examples of subsidy policy
• Single Regeneration Budget (SRB) in UK (Hodgson and Turner, 2003)
• Exemptions for at-risk group such as congestion price, and public service (Bonsall
and Kelly, 2005)
• Cheaper and free parking policy (Hu and Saleh, 2005)
• Public transport subsidy (Preston and Rajé, 2007)
• Bus patronage in UK (Lucas et al., 2008)
• The Transport Connections Program (TCP) in Australia (Lucas, 2012)
7. Examples of city and transportation planning
• Accessibility planning in UK (Social Exclusion Unit, 2003)
- Reviewing the regulations governing provision of bus services
- Integration of transport planning into planning for services provision
- To make transport more accessible, such as reducing cost and addressing the
fear of crime associated with public transport
- The formation of partnerships between transport providers, local authorities
and local service providers, such as education and health, and work on
transport solutions
• Transport Action Plan by Stockport Metropolitan Borough Council in Greater
Manchester (Hodgson and Turner, 2003)
- Giving voice, involvement and trust to the residents to create the policy
- Supporting organizing in the community (financial support for service,
goods, or young person’s leisure trip)
- The outputs of the process (development of bus, walkway or cycling)
• Congestion charging, and funding public transport service (Bonsall and Kelly, 2005;
Rajé, 2003)
• Improving the public transport service and making it more accessible for the at-risk
groups (Hu and Saleh, 2005)
• Relocation of key facilities (Bonsall and Kelly, 2005)
• Social transit in Victorian policy (Victoria, 2005)
• The new bus services in an outer Melbourne suburb (Bell et al., 2006)
• The multi-mode advocate (Rajé, 2007)
• Provision of healthy transport (Woodcock et al., 2013)
• Construction of new infrastructure for disability person (Mackett et al., 2008)
• Local Transport Plans (LTP) in UK (Lucas, 2012)
• The Transport Connections Program (TCP) in AUS (Lucas, 2012)
17
CHAPTER 3 LITERATURE REVIEW
3.1 Transport-Related Social Exclusion
The link between transport disadvantage and social exclusion is a topic that has been cumulatively
studied from the past. Transport disadvantage could induce social exclusion by barriers to employment,
exclusion from social services, fear and perceptions of safety, reduction in educational achievement,
and health service inequalities (Clifton and Lucas, 2004; Hine and Mitchell, 2017).
Transport disadvantage is associated with only some parts of social exclusion for the example, it is
possible that one is excluded but still be able to access to transportation, and vice versa (Currie and
Delbosc, 2010b). Transport disadvantage and social disadvantage are intercepted directly and
indirectly, causing shortage of transport that could lead to inaccessibility, followed by social exclusion.
Many previous studies sought to define factors which affect the feeling of social inclusion. For example,
(Lucas et al., 2011) explored the relationship between transport and social disadvantage of low income
in South Africa. The result shows that socio-demographic factors affect social inclusion such as gender,
age, and income. Based on the previous research, the characteristic of risk group to exclusion could be
elderly, low income, unemployment and no friend or family (Engels and Liu, 2011; Lucas et al., 2011;
Stanley et al., 2011). In addition, rural population tends to participate in less social activities (Shergold
and Parkhurst, 2012)
Transportation factors, Stanley et al. (2011) explored the association among a person’s travel patterns
assessed well-being in Australia. This research confirmed a significant association between increasing
mobility and reducing risk of social exclusion. Kenyon et al. (2003) concluded that lack of mobility
induced social exclusion. In addition, Stanley et al. (2010) found that Australians, who travel less
frequently, tends to deny social activities. Moreover, none driving group seems to participate in less
social activities (Currie et al., 2010).
Social inclusion could be promoted by transport policy. For instance, Mackett et al. (2008) applied the
AMELIA technique to examine if numbers of elderly who could access to the center of St Albans (CBD,
Great Britain) increased as a result of improving facilities along the walking way. The result shows that
there were higher numbers of elderly people who could reach the center of St Albans after the
implementation. Lucas et al. (2009) describes case-study research of four different transport projects
funded under the UK Department for Transport’s Urban Bus Challenge Fund (UBC) that UBC project
promote social inclusion. Transport policy not only induce social inclusion but also increase Well-being
of population. Nevertheless, some transport policies are able to increase social exclusion, such as road
users charging in Bristol (Rajé, 2003). Finally, summary of the examples of literature review on the
relationship between transportation and social exclusion is shown in Table 3-1.
18
Table 3-1: Summary of the examples of previous research of transport-related social inclusion
Authors Objective Methodology Conclusion Factor
(Kenyon
et al.,
2003)
To introduce a mobility
dimension to social
exclusion
Reviewing and
suggestion
There were high
correlations between
lacking access to mobility
and lacking access to
opportunities
-
(Rajé,
2003)
To find whether road
users charging in Bristol
can impact on social
exclusion
Interview of road
user
Road user charging
Induced social exclusion
Age, gender
(Mackett
et al.,
2008)
To analyse if the higher
numbers of elderly
persons who could go to
the centre of St Albans
in UK induced by the
improving facilities
along the walking way
AMELIA Higher numbers of elderly
people could reach the
centre of St Albans
induced by the improving
facilities along the walking
way
Age
(Lucas et
al., 2009)
To describe the case-
study of four different
transport projects
funded for Transport’s
Urban Bus Challenge
Fund (UBC), in UK
Qualitatively and
quantitatively
analyses from
data collected by
interviews with
end-users.
Urban Bus Challenge Fund
(UBC) could promote
social inclusion
Age,
employment
status, gender,
car owner ship
(Currie et
al., 2010)
To investigate the link
of transport
disadvantage to, social
exclusion, and
well-being in Melbourne
inter-disciplinary
approach and
Statistical analysis
Low income with no car
needed public transport
provision than walking in
order to prevent social
exclusion
Car
ownership,
income
(Stanley et
al., 2010)
To investigate person’s
travel patterns
and their risk of social
exclusion in Australia
interviewed 535
people from
Metropolitan
Melbourne,
Australia
Who travel less frequently,
and less distance had the
greater risk of social
exclusion, especially who
owned fewer cars and used
public transport less
frequently
Age, time,
frequency of
trip,
transportation
accessibility,
health, no.
family,
childcare
response
(Stanley et
al., 2011)
To explores
the association among a
travel pattern, their risk
of exclusion and self-
assessed
well-being
Statistical model
by quantitative
and qualitative
information
This research confirmed a
significant association
between increasing
mobility (frequently trip
making, activities) and
reducing
risk of exclusion
employment,
age,
education,
gender,
nationality
(Delbosc
and
Currie,
2011)
To explore the relative
influences of transport
disadvantage and social
exclusion on well-being
in Australia
empirical analysis
data from a travel
and disadvantage
survey in Victoria
Well-being of who faced
transport disadvantage was
lower than others
Employment,
occupation,
income, age
19
Authors Objective Methodology Conclusion Factor
(Engels
and Liu,
2011)
To investigate the
relationship between
elderly with non-driving
and social exclusion in
Melbourne
Statistical analysis
from data
collected from
seniors with
different
transportation
modes
Social exclusion occurred
in
non- driving elderly who
lived in a middle
ring area because they
difficultly went to bus
stop. Expansion of the area
of public transport service
could link them to social
activities
Age, non-
driving,
location
(Lucas et
al., 2011)
To explore
the relationship between
transport and social
disadvantage of low
income in South Africa
Quantitative
analysis
Transport is needed for
low-income
groups in South Africa to
prevent them from social
exclusions
Income,
employment,
gender, age,
number of
children
(Shergold
and
Parkhurst,
2012)
To investigate which
group of population
were risk to exclude
from social in Southwest
England and Wale
quantitative and
qualitative
data collected
from over 900
older persons
analyzed by
statistical analysis
Rural dwelling and older
age were both associated
with a higher risk of social
exclusion
Age, car
accessibility,
location
(Xia et al.,
2016)
To investigates
transport-related social
exclusion from a multi-
dimensional view of
Sydney and Perth,
Australia
Measuring the
transport inequity
by the Lorenz
Curve and Gini
index
The level of development
of the inner, middle and
outer cities were different,
causing the social inequity
which was related to issue
of social exclusion
housing
affordability,
employment
self-
sufficiency,
urban sprawl,
and transport-
mode share
3.2 Study Areas and the Index to Measure the Degree of Social Exclusion
As mentioned before, various previous studies in many areas of the link between transport disadvantage
and social exclusion developed several methods to measure and analyze the severity of social exclusion.
Many indexes to indicate the degree of feeling social exclusion were proposed, such as accessibility
level, mobility level, and trip frequency. The collected sample were also from various groups in the
society, including elderly and low-income people.
In this section, in order to understand the whole concepts of the previous studies to create the originality
of this paper, the previous analyzed methods, proposed indexes to measure degree of social exclusion,
what kind of samples, and studies areas were summarized as shown in Table 3-2.
20
Table 3-2: Literatures of how to measure the degree of social exclusion-related transportation
Au
tho
r
An
aly
sis
Met
ho
d
Evaluated factor used to measure social
exclusion Sampling
Stu
dy
are
a
Per
son
's s
pee
ch
Acc
essi
bil
ity
Mo
bil
ity
Tri
p f
requ
ency
Tra
vel
dis
tan
ce
Psy
cho
logic
al
sco
re
Eld
erly
Lo
w-i
nco
me
(Rajé, 2003) In-depth
interview X ✓ UK
(Ibeas et al., 2014) In-depth
interview X ✓ Brazil
(Ihlanfeldt, 1993) Spatial
approach X US
(Church et al., 2000) Spatial
approach X X UK
(Schönfelder and
Axhausen, 2003)
Spatial
approach X X ✓ ✓ German
(Matthews et al.,
2003b)
Spatial
approach X UK
(Páez et al., 2009) Spatial
approach X X X X ✓ Canada
(Mackett et al., 2008) Spatial
approach X ✓ UK
(Engels and Liu, 2011) Spatial
approach X ✓ AUS
(De Sousa et al., 2014) Spatial
approach X ✓ Portugal
(Ullah and Shah, 2015) Spatial
approach X ✓ Pakistan
(Özkazanç and
Özdemir Sönmez,
2017)
Spatial
approach X Turkey
(Hu and Saleh, 2005) Logit
model X ✓ Scotland
(Schmöcker et al.,
2006)
Logit
model X UK
(Stanley and Vella-
Brodrick, 2009)
Statistical
model X AUS
(Currie et al., 2010)
Structural
equation
model
X AUS
3.3 Transport Difficulty and Social Exclusion of Low Income
3.3.1 Transport difficulty of low-income group leading social exclusion
Non-low-income may make trips as frequently as wanted to join social activities; on the other hand, the
number of trips was limited for low-income. As a result, with less frequency of travel, low-income
housing tended to deny social activities, which consequently led to social exclusion problem (Lucas et
al., 2011). Since the fare has been increasing year by year (Goeverden et al., 2006), public transports
in many countries are subsidized by the governments. The inspiration was to decrease the burden of
transportation cost for low-income (Button, 2010).
21
A number of investigations on the linkage between social exclusion and affordability of low-income
have been cumulatively presented for decades. According to public transport subsidy programs in many
countries, the reduction of fare price could lead to higher travel demand (Goeverden et al., 2006; Witte
et al., 2006), as well as more social inclusion (Button, 2010; Lucas, 2012).
3.3.2 The previous transport subsidy policy to support low income for transportation
The car ownership rate has been increasing every day and people prefer to use automobiles. Thus,
congestion and pollution problems are occurred by numerous road users (Goeverden et al., 2006). The
motivation of promoting car users to change to use public transportations then comes up (Button, 2010).
One approach to encourage motorists to shift to use public transportations is fare subsidy program
(Goeverden et al., 2006). Rail subsidy not only leads to mode shift from other transport modes but also
generates new users (Folmer and Tietenberg, 2006). For instance, new users in public transport in
Brussels were 17.55%, 11.08%, and 13.69% from subsidy in tram, metro, and bus, respectively (Witte
et al., 2006).
In general, passengers select their public transport modes from fare price consideration. People change
transportation mode and new users typically increase when the subsidy policy begins (Abane, 1993).
Examples of promoting the use public transport by subsidy policy could be found in Netherland. Four
public transportations (bus, metro, train, and tram) have been subsidized for students by government of
the Netherlands since January 1, 1991. Public transport users and travel distances were increased by the
policy and student cars were also decreased by 34% (Goeverden et al., 2006). Summary of literature
review in the impacts of public transport subsidy and is shown in Table 3-3.
Table 3-3: Summary of previous research related to public transport subsidy
Authors Objective Transport policies Methodology Conclusion
(Pilegaard,
2003)
To investigate if an
increased in labor
supply resulted
from commuting
subsidy or not
1. Subsidies long-
distance commuting
2. Reduction of
transport taxes
CGE model Subsidies using could
reduce unemployment
but the result
congestion
(Goeverden et
al., 2006)
To evaluate 4 cases
of subsidy policies
in Belgium and
Netherland
1. Free public Leiden-
The Hague corridor
bus services
2. Free public bus in
the city of Hasselt
3. Free public transport
for all students in the
Netherlands
4. Free public transport
for students in the
Brussels region
Cost benefit
analysis
1. Free policy could
generate induce
demand
2. There were good
arguments for free
public transport for
specific groups such
as students and the
elderly
3. Full suspension of
subsidies has serious
impacts such as
more crowded, lower
quality of the
service, traffic
congestion
22
Authors Objective Transport policies Methodology Conclusion
(Witte et al.,
2006)
To examine the
effects of mode
shift and travel
behavior from
transport subsidy
in Brussels
Public transportations
are free service for
Flemish colleges
students
Quasi-
experiment,
Mental mapping
The policy could
generate induced
demand of Flemish
college students that
shifted from other
transport modes to free
train
(Piyushimita,
2011)
To investigate if an
increased in labor
supply in 23 states
in the U.S. resulted
from commuting
subsidy or not
The JARC program
(funding for transport
for low-income
workers)
Multi-level
mixed model
The JARC program
could lead to an
increase in labor
supply in terms of
higher employment
and higher wage
earning
(Moreno-
Monroy and
Posada, 2018)
To investigate if
home worker
commuted to the
company in CBD
more frequently if
commuting cost is
subsidized
hiring-costs subsidy
and commuting
subsidy
A spatial search
model
Commuting subsidy
for home workers is
still undesirable
However, without the issue of low income, transport subsidy policy might be not able to induce more
demand in some cases. For example, the commuting subsidy for informal worker (who tended to work
at home worker) could not persuade them to travel to the company more frequently because the
preference of working location also affected their decision to go to the company (Moreno-Monroy and
Posada, 2018).
3.3.3 The distribution of transport subsidy policy to target group
In general, the aim of public transport subsidy is to reduce burden of fare for low-income. When the
regulation was not efficiently defined, it was possible that non-low-income group would also access
these opportunities. However, some low-income persons may not afford or access to the services even
when it was subsidized. Then, the error of inclusion and the error of exclusion would occur (Estupinan,
2008).
An example of previous subsidy policies could be found in Mumbai, India. Twenty seven percent of
poorest population received only 19% of bus subsidy and 15.5% of rail subsidy. In addition, after public
transports were subsidized, 25% of these poor households did not access train and 10% did not travel
by bus. Hence, the distribution to low-income was not completed for this case (Cropper and
Bhattacharya, 2012)
To evaluate the error of distribution, Foster and Araujo, (2004) showed how to calculate the errors of
exclusion and inclusion. The error of exclusion is the percentage of target group (low-income, intended
benefit) that does not receive the benefits from the subsidy while the error of inclusion is the percentage
of the beneficial group who are unintended benefit. The errors of exclusion and inclusion can be
calculated by Equation (3-1) and Equation (3-2), respectively. The illustration of the errors of
exclusion (EOX) and inclusion (EOI) of subsidy policy is shown in Figure 3-1.
23
𝐄𝐎𝐗 = 𝐓𝐡𝐞 𝐧𝐮𝐦𝐛𝐞𝐫 𝐨𝐟 𝐥𝐨𝐰 𝐢𝐧𝐜𝐨𝐦𝐞 𝐩𝐞𝐫𝐬𝐨𝐧𝐬 𝐰𝐡𝐨 𝐝𝐨𝐞𝐬 𝐧𝐨𝐭 𝐫𝐞𝐜𝐢𝐞𝐯𝐞 𝐭𝐡𝐞 𝐛𝐞𝐧𝐞𝐟𝐢𝐭
𝐓𝐡𝐞 𝐧𝐮𝐦𝐛𝐞𝐫 𝐨𝐟 𝐭𝐨𝐭𝐚𝐥 𝐥𝐨𝐰 𝐢𝐧𝐜𝐨𝐦𝐞 𝐩𝐨𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧 × 𝟏𝟎𝟎 (3-1)
𝐄𝐎𝐈 = 𝐓𝐡𝐞 𝐧𝐮𝐦𝐛𝐞𝐫 𝐨𝐟 𝐧𝐨𝐧 𝐥𝐨𝐰 𝐢𝐧𝐜𝐨𝐦𝐞 𝐮𝐬𝐞𝐫𝐬
𝐓𝐡𝐞 𝐧𝐮𝐦𝐛𝐞𝐫 𝐨𝐟 𝐭𝐨𝐭𝐚𝐥 𝐮𝐬𝐞𝐫𝐬 × 𝟏𝟎𝟎 (3-2)
Figure 3-1: The illustration of the errors of inclusion and exclusion of subsidy policy (Foster and
Araujo, 2004)
3.4 Transport Difficulty and Social Exclusion of Elderly
3.4.1 Transport difficulty of elderly leading social exclusion
Aging populations have become a global phenomenon. From 2006 through 2050, there will be a
dramatic growth in the number of people 60 years old and over, from 11% to 22% (World Health
Organization, 2007). In the US, the Baby Boom generation born between 1946 and 1964 began to turn
65 in 2011. In the near future, the US will experience significant growth in its population aged 65 and
over, from 43.1 million in 2012 to an estimated 83.7 million in 2050 (Ortman et al., 2014).
However, the common concern is that older people’s low mobility often limits their transportation
ability (Jansuwan et al., 2013; Kneale, 2012; Wong et al., 2018). Difficulty with transportation is a
major factor leading to lower trip frequencies for many elderly, resulting in a decrease in their degree
of community participation (Currie and Delbosc, 2010b; Tansawat et al., 2017). It has been found
that the reasons that the mobility of seniors is more limited than other age groups include their physical,
financial and emotional conditions, such as poor walking ability (Aguiar and Macário, 2017;
Jansuwan et al., 2013; Kneale, 2012; Schwanen et al., 2012). The elderly not only face challenges in
their use of public transportation but also feel anxiety when driving; they subsequently become either
more reliant on others or become more excluded from society (Jansuwan et al., 2013). In addition, the
living conditions of some elderly discourage them from going out to join society. For example, many
elderly are likely not only to be widowed, but are also living in smaller families than younger
24
generations were, resulting in a lack of family members who can support them with their travel needs
(Endicott et al., 1993). However, some previous studies have indicated that not every elderly person
lacks mobility. Some seniors who have more free time and have saved more income than other groups
make more trips than younger people, especially recreational trips (Hahn et al., 2016; Leitner and
Leitner).
The dimensions of social exclusion differ by age group. Whereas the social exclusion of younger people
is commonly evaluated using education and family support, the social exclusion of working-age people
generally focuses on employment and income level (Aldridge et al., 2011; Department of Social
Security, 1998). However, the dimensions of the social exclusion of older people (generally, those who
are retired) highlight such people’s level of access to basic needs, degree of social participation, and
transportation ability (Jansuwan et al., 2013; de Sousa et al., 2014).
To maintain the degree of social participation of elderly, many changes in older people’s living
environments and conditions are needed to support their transportation. This problem occurs not only
in developed countries but also in developing countries (Oizumi et al., 2006; UNFPA Thailand, 2006).
3.4.2 Ride sharing program for supporting travel needs of elderly living in the area with poor
transportation access
In many communities, local authorities have established volunteer programs to support the mobility of
the elderly. In global north countries, such as the US, Canada and Japan, governments have created
programs to encourage volunteer ride-sharing drivers to inquire about elderly residents who need door-
to-door service to reach their destinations (Campbell, 2015). To support seniors, the cost per ride has
often been subsidized by the government to the level at which it is cheaper than a taxi ride (Campbell,
2015). Ride-sharing services have generated a higher confidence level with regards to travel; in contrast,
taxi services suffer from uncertain quality, and some taxi drivers typically avoid servicing seniors
(Campbell, 2015).
In some cities such as in California, USA, some volunteers have joined an ‘Uber for seniors’ program.
They were training to be health care professionals and had sufficient knowledge of elderly psychology
and ailments to support the elderly when needed (Schiller, 2014). In Japan, Uber’s expansion in large
cities such as Tokyo has been restricted by public transportation regulations. Nevertheless, the Japanese
government has encouraged Uber drivers to service seniors living in rural areas where taxi and public
transportation are not sufficient (Demetriou, 2016).
3.5 The Concept Hard and Soft Infrastructure Policies
In developed and developing countries, the development of infrastructure is always important.
Infrastructure systems are necessary in progressing economic progress of the global. The entirely
sections of the economic are influenced by of infrastructure systems, such as transportation system and
communicate lines (Gu, 2017).
25
There are generally two categories of civil development concepts. First, the ‘hard-infrastructure
concept’ refers to the main physical networks, which is necessary for the functioning of the country,
while the ‘soft-infrastructure concept’, focuses on the institutions, required to maintain the systems,
such as economic, health, and cultural and social standards. The example of the systems to maintain are
the financial system, the education system, the health care, the system of government, and law
enforcement, as well as emergency services (Hamutuk, 2017).
For the viewpoint transportation, hard infrastructure mainly focuses on the construction of infrastructure
or new transportation system, such as railway and motorway, but soft infrastructure generally refers to
transportation management, such as convenience improvement, traffic management and service
frequency. The examples of hard and soft infrastructure development for logistic system by Perez and
Wilson (2010) are written below.
• Maples of Hard Infrastructure:
- Physical infrastructure, measured for the level of development and quality, such as
ports, airports, roads, and rail infrastructure.
- Information and communications technology (ICT), also interpreted as the degree of
economy, using information and communications technology to improve effectiveness,
and production, as well as to decrease transaction budgets. It consists of indicators on
the reliability, availability, and government prioritization of that ICT system.
• Example of Soft Infrastructure:
- The improvement of efficiency of transportation aiming for enhancing the level of
efficiency of transportation, represented by transport cost, time and amount of export
and import products.
- Regulatory environment and business measuring the level of development of
transparency. For example, it is created on the indicators of preference, measures to
fighting corruption, administration transparency, and irregular expenditures.
3.6 Literature-Related Concepts of Statistical Model Used in The Study
This study applied various kinds of statistical discrete choice model, which are binary logit model,
ordered logit model, and count data regression model for the analyses. This section introduces the
concept of those models as below.
3.6.1 Binary logit model
The binary logit model is one of the discrete choice models applied to examine the relationship between
independent variables and two choices of dependent variable, such as yes or no. The probability of the
possible outcomes was predicted using logistic function of the explanatory variables (Greene, 2009)
where F(Z) is a sigmoid (S-shape) function, and Z is a function of explanatory variables, as shown in
Figure 3-2.
26
Figure 3-2: The logistic function
3.6.2 Ordered logit model
To predict an ordinal dependent variable affected by independent variables, Ordinal logistic regression
is applied. Ordinal logistic regression can be considered as either as a generalization of binomial
logistic regression and a general multiple linear regression. Same as other kinds of regression model,
ordinal regression uses the relations between independent variables to predict the dependent variable
(Greene, 2009).
Dependent variable should be measured as an ordinal variable but independent variables can be ordinal,
categorical or continuous. The probability of the event is represented by the ordinal level as shown in
Figure 3-3 (𝑷𝒊 is probability). Examples of ordinal variables include ranking classes (e.g., a three-
point scale of degree of satisfaction with a product of customer, ranging from ‘dissatisfy, to ‘fair’, to
‘satisfy’) or Likert scales (e.g., a seven-point scale from ‘strongly satisfy’ to ‘strongly dissatisfy’ with
transportation service).
Figure 3-3: The structure of multiple ordered choices
3.6.3 Count data regression analysis
To meet the common assumption of the normal distribution of variance and residual errors for linear
regression model, when a continuous dependent variable is skew, a conversion of this dependent
variable can result in errors that can be approximated as normal. Nevertheless, if the dependent variable
is discrete or categorical, a simple conversion cannot produce normal distribution of variance and errors
(Long, 1997).
27
When the dependent variable is the counted data or number of occurrences of any event, such as the
number of trip frequency and road accident, the distribution of this counts is not continuous, but it is
discrete, and limited to non-negative values. There are various choices applied to estimate the number
of occurrence, such as Poisson regression and negative binomial model.
3.6.4 The best fit of model for discrete choice model
In general, the best fit logit model was selected from the highest by Pseudo-R² (rho-square; ρ²) and
maximum log-likelihood estimation (Cohen et al., 2013). Adjusted rho-square is the rho-square
adjusted by penalty term of increasing variable. The problem of Pseudo-R² comparison is that they
always improve when more variables are added in the model. So Adjusted rho-square was developed
by trading of improving log-likelihood against the including variables (Ben-Akiva and Lerman,
1985). Adjusted rho-square can be calculated by the Equation (3-3) below.
𝛒𝐚𝐝𝐣𝟐 = 𝟏 – [𝑳𝑳(𝛃𝐌𝐋𝐄) – 𝐤]/𝑳𝑳(𝟎) (3-3)
• where LL(0) is Log-likelihood estimation when predicted probability of dependent choices (y)
are equal, LL(βMLE) is the maximum log-likelihood estimation, and k is the number of
parameters used in model
Log-likelihood ratio test (LRT) is an examination of significant difference between restricted and
unrestricted log it model by using chi-square test from Equation (3-4) below (Ben-Akiva and
Lerman, 1985).
2[LL(βR)-LL(βU)] ~ χ² (kU-kR,1-α) (3-4)
• here LL (βR) is maximum log likelihood estimation of restricted model, LL(βU) is maximum
log likelihood estimation of unrestricted model, kU is the number of parameters in unrestricted
model, kR is the number of parameters in restricted model, and 1-α is 95% confident level (p-
value = 0.05)
For all discrete dependent variable models, AIC (Akaike information criterion) is a measure of
the goodness of a statistical model (Akaike, 1974). The preferred model is the model with the
minimum AIC value that could be calculated by the Equation (3-5) below
𝑨𝑰𝑪 = 𝟐𝒌 − 𝟐𝑳𝑳 (3-5)
• Where LL is the maximum log-likelihood estimation and k is the number of parameters used
in model
BIC (Bayesian information criterion), developed by Akaike and by Schwarz in 1978, has stronger
penalty term of additional parameter than those in AIC (Akaike, 1974). The preferred model is the
model with the minimum BIC value that can be calculated by the Equation (3-6) below
𝑩𝑰𝑪 = 𝐥𝐧 (𝑵)𝒌 − 𝟐𝑳𝑳 (3-6)
28
• Where LL is the maximum log-likelihood estimation, k is the number of parameters used in
model, and N is the number of observations
3.7 Structural Equation Model (SEM)
3.7.1 The concept of SEM
To analyze many relationships among number of variables simultaneously, Structural equation
modelling (SEM) is applied as a multivariate statistical analysis method. SEM technique is a
combination of the factor analysis, multiple regression analysis and path analysis (as shown in Figure
3-4), used to analyze the structural relationships between explanatory and latent groups of variables.
There are two types of variables used in the model, which are endogenous and exogenous variables.
This method estimates the relationship among number of variables in a single path analysis. Exogenous
variables are considered as independent variables, while endogenous variables are measured as
dependent variables. SEM is applied on theoretical construction in many academic field, such as social
science and economic (Hox et al., 2017).
Figure 3-4: The analysis techniques used in SEM
SEM technique is assessed relying the covariance structure analysis. The parameters are estimated by
variance–covariance matrix, following the constraints of the model. SEM is applied to predict the
influences of the exogenous variables on the endogenous variables. SEM also permits specification of
error-term of covariance. The fundamental of SEM is the construction of Latent variable as shown in
Figure 3-5, when the observation is expressed by Equation 3-7 and 3-8. The Equation 3-9 (Maximum
likelihood: ML) and Equation 3-10 (Generalized least mean square: GLM) are applied to estimate the
parameter of the model.
Figure 3-5: The analysis techniques used in SEM
𝑽 = 𝑨𝒇 + 𝒆 (3-7)
29
(3-8)
• Where V is Observed variable, A is Factor loading matrix, fis Latent variable, and e is Residual
vector
𝒇(𝜽) = (𝒏𝒙 − 𝟏) [𝒕𝒓(∑(𝜽)−𝟏𝑺) + 𝒍𝒏|(𝜽)−𝟏𝑺| − 𝒏𝒙] (3-9)
𝒇(𝜽) =(𝒏𝒙 − 𝟏)
𝟐𝒕𝒓 [{(𝑰 − 𝒔−𝟏 ∑(𝜽))} {(𝑰 − 𝒔−𝟏 ∑(𝜽))}
′
] (3-10)
• Where S is the sample covariance matrix for the observed data, nx is number observed variable,
I is unit matrix, and ∑(𝜽) is population covariance matrix implied by the model with
parameters 𝜽.
3.7.2 Assessing Goodness-of-fit of SEM
Numerous indicators were created to evaluate the goodness of fit of SEM. Most criteria base on the
application of chi-square statistic, given by the sample size and the goodness of fit to chi-square function
(Golob, 2003). First, goodness-of-fit for a single SEM bases on the measurement of root mean square
error of approximation (RMSEA). The appropriate value of RMSEA should be less than 0.05 (Browne
and Cudeck, 1992). However, it is also acceptable if the value of RMSEA is slightly greater than 0.5
but should not be greater than 0.8 (Byrne, 2016). For other goodness-of-fit indexes, baseline
comparison such as comparative fit index (CFI) and normed fit index (NFI), the suitable model should
have those value greater than 0.90 (Bentler, 1990; McDonald and Marsh, 1990). However, it is also
acceptable if the values of NFI, CFI range from zero to one (Byrne, 2016).
3.7.3 The statistical software used for the analysis of SEM
To analyze number of relationships among various variables simultaneously, many statistical software
has been developed, such LISREL, AMOS and EQS. However, it would be different of the analyses of
SEM by using different programs. Byrne (2001) compared the analyses of SEM by using different three
SEM computer programs, which were LISREL, AMOS and EQS. The comparisons focused on
difference of the testing of model specification, preliminary analysis of data and the CFA models.
(Nachtigall et al., 2003) summarized the efficiency of using these three programs (LISREL, AMOS
and EQS). This study also defined that LISREL is the appropriate primary software; on the other hand,
the advantage of AMOS and EQS are the easiness of the use.
30
3.8 Kishi’s Logit PSM (KLP)
3.8.1 The development of KLP
The price sensitivity measurement (PSM) method has been applied to evaluate customer perceptions
when observing four product price levels, including ‘reasonable’, ‘expensive’, ‘too expensive to buy’
and ‘too cheap to buy’ as shown in Table 3-4 and Figure 3-6. In PSM approach, the four price levels
reported by respondents would be used to generate cumulative frequencies as shown in Figure 3-7 (a).
The complementary ‘Reasonable’ and ‘Expensive’ frequencies are shown in Figure 3-7 (b).
Subsequently, to analyze the perception of customers, the cumulative frequency of ‘Reasonable’ and
‘Expensive’ is inverted into ‘Should be less expensive’ and ‘Should be more expensive’, as shown in
Figure 3-7 (c) (Kishi and Sato, 2005).
However, acceptable price ranges could not be determined using only the PSM approach. By applying
The Kishi’s Logit (KLP), a modified version of PSM, four relative cumulative frequencies were
regressed using the continuous function of the binary logit model. These functions were used to
determine acceptable price ranges and standard prices, as shown in Figure 3-7 (d).
Table 3-4: Four prices and the Questions
1. Reasonable What price do you think would be reasonable for the product?
2. Expensive What price do you think would be too expensive for the product?
3. To expensive to be
willing to buy
What price do you think would be too expensive to be willing to buy the
product?”
4. To expensive to be
willing to buy
What price do you think would be too cheap to be willing to buy the
product, because of doubts about its quality?
Figure 3-6: Price Sensibility and Willingness to Buy
31
Figure 3-7: The price indicator references of PSM and KLP (Kishi and Sato, 2005)
(c)
(b)
(a)
(d)
32
3.8.2 The usability of KLP
There are various applications of KLP stated by (Kishi and Sato, 2005) as written below.
• Identifying standard price
- When clients buy a service, they assess its quality in contradiction of the cost. If they
feel the cost of the service is too expensive for clients, they will not buy the service.
When the feel the price is too cheap, they will doubt if the quality of the service would
be inadequate. Suppliers could set the acceptable price of the service for their target
customers by applying KLP. Applying KLP can develop upcoming marketing plans by
relating setting the service price.
• Approximation of market size
- The referenced price indicator analyzed by KLP, a dissimilar viewpoint shows a
categorized market to which a service belongs, which makes it possible to analyze how
the product is evaluated in the categorized market. A market is categorized Premium
Market and to Discount Market, as shown in Figure 3-8.
Figure 3-8 Estimation of Market Size by KLP
- Premium market: When customers who feel the price of service inside the range
between Standard Price and Maximum Price is too expensive to purchase are removed
from those clients who feel it is expensive, the remained group of customers can be
considered as Premium Market. This group is potential purchasers who will buy the
product despite feeling it is expensive and they are generally noticed in the market such
as brand product. Multiplying the price of the service by the amount of potential
demand (potential purchasers) related to the rate of Premium Market gives potential
sale volume in the market.
33
- Discount market: When clients feel the price of service inside the range between
Standard Price and Minimum Price is too cheap to buy are removed from those clients
who feel it is reasonable to buy, the remained group of customers can be considered as
Discount Market. This group is the group of potential clients who will purchase the
product when they feel its price reasonable. Multiplying the price by the amount of
demand (potential purchasers) related to the rate of Discount Market gives potential
sales volume in the market.
3.8.3 The application of KLP in the previous researches
KLP has been extensively applied to assess acceptable price ranges. For example, in 2002, the
acceptable cost for snow removal for Japanese residences that varied with different levels of service
was examined by Kishi et al. (Kishi et al., 2002). In 2003, Kishi and Satoh evaluated the acceptable
cost of low-population vehicles in Sapporo and Tokyo in Japan; the result showed that the actual
average price of low-population cars was higher than customer perceptions of a reasonable price (Kishi
and Sato, 2005). Kishi and Satoh also applied KLP to examine the value of safety on the willingness
of drivers to use the Doto expressway (Kishi and Sato, 2010); subsequently, Iwadate et al. used a
similar method to find an acceptable toll price for this expressway in 2013 (Iwadate et al.). In 2015,
The comparison of acceptable metro fares between Bangkok, Thailand and Manila, Philippines was
studied by Baron and Choocharukul (Baron and Choocharukul, 2015). In addition, not only the price
but also other types of scale values, such as an acceptable walking distance for access, can be analyzed
by KLP. For example, in 2012, pedestrian access walking times and distance thresholds to urban metro
stations were investigated by Yann et al. (Yang et al., 2013).
3.9 Intercepted Interview Approach
In this paper data were collected by intercept interview approach. Intercept interview has many
advantages consisting of ability to approach the right target group, ability to survey immediately, and
ability to obtain more details of information (both accuracy and reliability) from respondents (Schaller,
2005). Intercept survey has also been defined to provide better access to the segment of urban
populations that is harder to meet such as low-income or younger group than general survey methods
such as e-mail, telephone, face-to-face and mail (Rotheram-Borus et al., 2001).
Retro perspective questions were used in the questionnaire in this study. Retro perspective question is
the questions to recall the memory of respondents. The accuracy of their memories can be varied by
different respondents. Some studies suggested that the memories and ability to recall may be biased
even within short time. In addition, the accuracy may be continuously decreasing with the time. The
retrospective is vulnerable to 1) Social desirability: respondents answer what they think the interviewer
wants; and 2) Accuracy: it can be fluctuated (Nisbett and Wilson, 1997). Therefore, the recall period
should be a point of time that is easy to remember such as a large festival or holiday or a date on which
something memorable occurred. Researchers should not use general time period such as 2 months or 4
months ago. Data have to be collected within a short period of time after the intervention in order to
avoid recall decay (Piyushimita, 2011).
34
3.10 Summary of Literature Review
In this part, the compilation of literatures of transportation-related social exclusion, the current situation
of transport difficulty elderly and low-income Thailand, the examples of previous policies to support
those people for travel needs, and the related previous studies, as well as the analyses methods applied
in this study, were reviewed in this chapter.
First, the author would emphasize that it is clear that most of the previous studies of the relationship
between transport disadvantage and social exclusion have been conducted in developed countries,
where social issues usually receive high attention from the government or other related organization.
However, this study highlights data collected in Thailand, a developing country.
Second, the majority of former studies evaluated social exclusion based on existing travel ability, which
sometimes varied according to other transportation policy scenarios. Current transportation ability
might not accurately represent a person’s desired level of transportation. Therefore, to obtain a
satisfactory level of social participation, this study focuses on not only existing but also desired travel
abilities.
Finally, for the previous study on unsatisfactory transportation-related social exclusion of low income
and elderly, few studies have applied psychological indicators to measure the degree of social exclusion,
and little attention has been paid to the relationship between psychological scores and the desired level
of transport ability. In addition, whereas previous studies applied psychological indicators to general
age groups, this study adopts psychological questions from questionnaires specific to older people's
quality of life and well-being (Bowling et al., 2013; Endicott et al., 1993; Kaneda et al., 2011;
Kneale, 2012; Raphael et al., 1995), which enables a focus on the dimension of social exclusion among
the elderly.
35
CHAPTER 4 CURRENT SITUATION OF TRANSPORTATION IN
BANGKOK
4.1 Outline of Bangkok
Bangkok is the capital city of Thailand recognized as Krung Thep. Bangkok city occupies 1569 square-
kilometer with 50 districts. Chao Phraya River is the main river cutting through the center of Bangkok
dividing Bangkok in to west and east sides as show in Figure 4-1. Bangkok has population around 8.28
million people, or 12.6% of Thai population which is considered to be very populous city. Bangkok in
considered to be the center of Thai economic, cultural and social activities and various ranges of citizens
are living in Bangkok (BMTA, 2018).
Figure 4-1: Map of Bangkok
The city center (high density residential and commercial areas) is located in the middle of Bangkok.
The city's urban sprawl spreads into sections of the six adjacent provinces that is called metropolitan
area. Although Bangkok is the capital city of Thailand, there is still around 60% of rural and low density
residential areas (green and yellow areas) as show in Figure 4-2.
36
Figure 4-2: Land use of Bangkok
The city of Bangkok is locally governed by the Bangkok Metropolitan Administration (BMA). The
transport policy implemented in Bangkok are mainly under the Ministry of Transport (MOT) and the
Office of Transport and Traffic Policy and Planning (OTP) (BMTA, 2018).
4.2 Transportation Systems of Bangkok
Totally, Mode share of transportation systems in Bangkok, Bangkok is car dominated city. Due to the
urban’s sprawl and limited public transport system, most Bangkok citizen need to rely on their own
private vehicle for transportation. However, there are portion of people travelling by ground
transportation which are bus and metro, as well as paratransit. Mode share of transportation of Bangkok
in 2017 is illustrated in Figure 4-3 (BMTA, 2018).
Figure 4-3: Transportation mode share of Bangkok
56%25%
11%
5%
1% 2%Private car
Buses and van
Metro
Paratransit (taxi, tuk tuk
and public motorcycle)
Walk and bike
Others
37
4.2.1 Road system
Bangkok city has multiple transportation systems. Canals of Bangkok served as a major transportation
mode in the past, but they have been changed to important land transportation. Road is the primary
mode of travel in Bangkok which has 48 major roads including expressway with branching in to small
street (soi) to serve local neighborhood as shown in Figure 4-4 (Pink line = expressway; red line = loop
line; yellow line = major highway). Bangkok has eleven major bridges cross over the Chao Phraya
(major river of Bangkok) linked the two sides of the city (east and west sides).
Figure 4-4: Road system of Bangkok
Bangkok road system has been rapid developed since 1980s caused rapid increases in vehicle ownership
and traffic demand (Ministry of Transport, 2018). The most of registered vehicle of Bangkok are car
and motorcycle, followed by truck and public transit as shown in Figure 4-5 and 4-6.
Figure 4-5: Number of register vehicle in Bangkok
0
2000
4000
6000
8000
10000
2003 2005 2007 2009 2011 2013 2015 2017
1000 v
ehic
le
Year
38
Figure 4-6: Proportion of registered vehicle of Bangkok in 2018
4.2.2 Public transportation system
A) Transit system
Transit operation consists of 9623 buses and 5284 vans servicing 500 routes as shown in Table 4-1
(Bangkok Mass Transit Authority, 2016). There are various bus types serviced in Bangkok as shown
in Figure 4-7. The major bus route of Bangkok is shown in Figure 4-8. Besides, Bangkok Bus Rapid
Transit (BRT) includes isolated rights of way and closed stations, which have been operated for only
15.9 kilometers with 12 stations as shown in Figure 4-9. BRT has failed in the extension of services
due to low demand, road capacity and land use in Bangkok (Wu and Pojani, 2016). However, the
number of transit users tends to be decrease as expressed in Figure 4-10.
Table 4-1: Number of vehicle and service route of transit system of Bangkok
Types Buses Routes
BMTA buses 2774 114
Private joint bus buses 3621 94
Minibuses 994 42
Shuttles in sois 2234 101
AC Microbuses 5284 149
Car
44.93%
Truck
15.46%Taxi
0.82%
Motorcycle
37.29%
Motorcycle taxi
and tuk tuk
1.05%
Public bus and
microbus
0.45%
39
Non-AC Bus AC Bus
Minibus Microbus
Figure 4-7: Buses types
Figure 4-8: Major bus route of Bangkok
40
Figure 4-9: The previous BRT route in Bangkok
Figure 4-10: Bangkok transit ridership per year
B) Rail system
There are two major organization operating metro system f Bangkok which are BTS and MRTA. Until
now, BTS (since 2000) and MRT (since 2005) metros are operated on 5 lines covering 112 kilometers.
Metro lines are connected with transit routes into the major ground public transport network in Bangkok
(Mass Rapid Transit Authority of Thailand, 2016). The metro system of Bangkok is shown in Figure
4-11 and Figure 4-12. For rail system, Bangkok people tend to use train system of the State Railway of
Thailand. Ridership of metro tends to increase every year as shown in Figure 4-13.
Bangkok people also travel by public train operated by the State railway of Thailand. The railway covers
not only Bangkok but also other cities in Thailand as shown in Figure 4-14 and 4-15. The rider ship of
public train is shown in Figure 4-16.
0
50,000
100,000
150,000
200,000
250,000
2010 2011 2012 2013 2014 2015 2016
10
00
per
son
Non-AC Bus AC Bus BRT
41
BTS train MRT train
Figure 4-11: Bangkok transit ridership per year
Figure 4-12: Metro routes of Bangkok
Figure 4-13: Bangkok metro ridership per year
0
50000000
100000000
150000000
200000000
250000000
300000000
2010 2011 2012 2013 2014 2015 2016 2017 2018
BTS MRT
42
Figure 4-14: Public train
Figure 4-15: Public train map
Figure 4-16: Public train ridership
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
2009 2010 2011 2012 2013 2014 2015 2016 2017
10
00
per
son
43
C) Water system
Bangkok people also commute or travel by public boat along Cha Praya river, the major river of
Bangkok as show in Figure 4-17 and 4-18. However, the number of passenger is quite small.
Figure 4-17: Bangkok public boat
Figure 4-18: Public boat station of Bangkok
D) Paratransit
There are many kinds of paratransit service in Bangkok operated by both public and private sectors.
The major paratransit vehicle types in Bangkok are taxi, tuk tuk and motorcycle taxi as shown in Figure
4-19.
44
Taxi Tuk tuk Motorcycle taxi
Figure 4-19: Bangkok paratransit vehicle
4.3 Current Transport Policies to Support General Low-Mobility Group in
Bangkok
Since transport difficulty of Bangkok low-mobility people has become a major obstacle for going out
to participate in social activities, services and opportunities. To prevent them from being excluded from
the society, the government has created various kind of transport policies in order to support them for
travel needs.
For example, door-to-door service, demand responsive transit (DRT) and transport assistance program
have been provided for disability people. Most of the users tended to use the services to go to hospital
and shopping mall (Bangkok Enterprise, 2018). The Bangkok government aimed to provide the extra
transport facilities to support disability, such as low-floor bus (Fredrickson, 2014). The service center
of disability people (disability hub) were also establish in many areas in Bangkok as an activity and
community for them in Bangkok (ThisAble, 2018). For elderly, the government is also trying to provide
other forms of support, such as passenger cabins, the improvement of access walkway conditions, travel
assistance, and simpler ticketing information systems for elderly, but these facilities have not yet been
fully yet developed in Bangkok (Bangkok Mass Transit Authority, 2016; BTS, 2016).
For young children, parents generally take care of them for transportation. However, in the case of
limited free time of other living conditions of their parents, the government has provided door-to-door
service (e.g. carpool, van and bus) to support them for travel in the specific route, such as going to
school (Department of Education, 2016). To support the issue of unaffordability of public
transportation fare of low-income population, public transport subsidies, such as free bus and train, were
introduced in Bangkok. However, everybody can use this free service even they are not low-income
(State Railway of Thailand, 2014).
4.4 Transport Difficulty and Social Exclusion of Low Income in Bangkok
4.4.1 The success in poverty reduction in Thailand
Over the last four decades, Thailand has made outstanding development in economic and social
development, moving from a low-income to a higher-income country in only a few decades. Thailand
has been one of the extensively mentioned as an example of development success, with remarkable
reduction of poverty, particularly from 1980 (World Bank, 2017).
45
The economy of Thailand raised at an annual rate of 7.5% in the flourished years (from 1960 to 1996),
and 5% after the Asian financial crisis (from 1999 to 2005), producing lots of employments that helped
pulling lots of low-income population out of poverty. The development together with multi-dimensions
welfares have been remarkable. For example, larger number of children are receiving higher education,
and more population are covered by health insurance while the social security has also improved. After
average economic growth of Thailand slowed down to 3.5% (from 2005 to 2014), with an incline to 2.3
% from 2014 to 2016, Thai economic is nowadays on the pathway to recovery back. The economic
growth is reached 3.5% in 2017 when the everage monthly income of Bangkok people was 157223
JPY. Totally, the poverty rate reduced considerably over the last 30 years from 67% in 1986 to 7.2% in
2015 (World Bank, 2017).
4.4.2 The challenge of increasing transportation affordable for low-income group in Bangkok
However, poverty and inequality are still a challenge in Thailand, with vulnerabilities as a result of
faltering economic growth, falling agricultural prices, and ongoing droughts. Based on the previous
record in 2014, an additional 6.7 million people were living within 20% above the Thai national poverty
line but remained risk to dropping back into the condition of poverty. In addition, even though inequality
level has been reduced from the last 30 years, the inequalities of personal income level can be still seen
not only in rural area but also in Bangkok, the capital city of Thailand (World Bank, 2017).
In the case of Thailand, public transportation fare has become more expensive year by year but the job
opportunities and average income of those low-income people has not been proportionally with the
fares. The fares of ground public transportation, which are buses, van and metro have been increased
since past few decades ago, according to Bangkok Mass Transit Authority (BMTA), the city’s bus
service provider and Mass Rapid Transit Authority of Thailand (BMTA, 2018; BTS, 2017). Although
medium or higher-income groups were not significantly affected by the additional fares, it caused the
lots more additional transportation burden of low-income people that may lead to the arising of inequity
I the society. This issue became the crucial issue in Thailand, and subsequently the government offered
the supporting fund to the department of transportation to resolve this transport burden problem of low-
income group, which is discussed in the next section (Ministry of Transport, 2014).
4.4.3 Public transport subsidy in Thailand to support low-income in Bangkok
Recently, to reduce transportation burden of low income population, Thai government cooperated with
the organizations of the ministry of transport have created public transport subsidy program as follow.
A) Free train policy
On August 1, 2008, free train policy was created by the Thai government, and cooperated with the SRT.
The objectives of the policy were to provide an alternative of public transportations for low-income,
and to promote the use of public train. The free train policy covers only the third-class seats (The lowest
quality of seats) in 172 lines, including 30 lines to the north, 36 to the north-east, 30 to the east, 42
Maeklong-local lines, and 34 lines to the south and the west as shown in Figure 4-20. The fare is
subsidized by the budget from taxation of Thai citizens. Everybody can access free train at every station
without any conditions. According to satisfactory of the implementation, free train policy has been
extended from January 1, 2009 to 2014. Until 2013, there have been 160,125,508 free train users since
46
the policy was launched. The total amount of subsidy by Thai government to SRT was 4,678,422,546
Baht. The financial status of free train policy is shown in Table 4-2 (State Railway of Thailand, 2014).
Figure 4-20: Rail system of Thailand
The other policy that came together with free train policy was free bus policy. The policy was found by
Thai government with BMTA on August 1, 2008. Non-air-conditioned buses in 73 routes all over
Bangkok Metropolitan have been in services with no fair. The objective of the free bus policy was the
same as those of free train policy. Moreover, the free bus could support the intermodal transportation
between free trains and free buses. Until 2013, the total amount of subsidy by Thai government to
BMTA was 10,475,120,000 Baht (State Railway of Thailand, 2014).
47
Table 4-2: Time series, number of users, and subsidy budgets of free train policy in Thailand
Phase Period of
implementation Duration
The number of free
train users
Amount of Subsidy by Thai
government (JPY)
1 1/8/2008 to
31/1/2009 6 months 19,084,057 5,743,784,902
2 1/2/2009 to
31/7/2009 6 months 16,466,188 5,804,859,414
3 1/8/2009 to
31/12/2009 5 months 14,567,600 5,044,048,222
4 1/1/2010 to
31/3/2010 3 months 8,413,608 2,980,963,558
5 1/4/2010 to
30/6/2010 3 months 8,207,682 3,176,934,674
6 1/7/2010 to
31/12/2010 6 months 16,962,837 6,057,256,556
7 1/1/2011 to
28/2/2011 2 months 6,037,450 2,033,668,650
8 1/3/2011 to
30/6/2011 4 months 11,725,005 4,464,414,837
9 1/7/2011 to
15/1/2012
6 months
15 days 17,544,049 5,973,145,994
10 16/1/2012 to
30/4/2012
3 months
16 days 6,743,027 2,315,722,155
11 1/5/2012 to
30/9/2012 5 months 10,591,442 3,590,053,164
However, according to the record of Thai public transportation in 2013, the share of public train was
still low compared to other sectors of public transportations. The proportion of passengers was divided
into 52.33% travelling by bus, 1.47% travelling by coach (intercity bus), 12.97% travelling by subway,
5.72% travelling by public train, 21.20% travelling by air transportation, and 6.31% travelling by water
transportation (Ministry of Transport, 2014).
Thai government aims to create more the adequacy of transportation policy to support those low-income
further. Not only transportation policy but also the integrated policy from other sectors, such as the
strategic plan to improve job employment, income and social opportune for low-income group will be
considered for social sustainability in the future.
B) Free bus policy
Since August 1, 2008, free bus policy has been implemented by the ministry of transport cooperated
with Bangkok Mass Transit Authority (BMTA) with the same objective of supporting low income same
as free train policy. The detail of the program is that 114 bus routes (fan-cooled or non-air-conditioned
buses) operated by BMTA are serviced without fare. This program is also funded by using tax of Thai
population. The first implementation was decided for only 6 months, but it was prolonged until now.
The operation report indicated that 5024.34 million baht had been subsidized until September 30, 2013
as shown in Table 4-3 (BMTA, 2017).
48
Table 4-3: Time series, number of users, and subsidy budgets of free bus policy in Thailand
Pha
se
Period of
implementation Duration
The number of free
bus users
Amount of Subsidy by Thai
government (JPY)
1 1/8/2008 to
31/1/2009 6 months 90,070,000 5,887,666,500
2 1/2/2009 to
31/7/2009 6 months 92,810,000 5,907,331,500
3 1/8/2009 to
31/12/2009 5 months 77,350,000 5,327,766,000
4 1/1/2010 to
31/3/2010 3 months 45,360,000 2,951,268,000
5 1/4/2010 to
30/6/2010 3 months 45,520,000 3,030,273,000
6 1/7/2010 to
31/12/2010 6 months 95,340,000 6,100,531,500
7 1/1/2011 to
28/2/2011 2 months 29,960,000 2,037,708,000
8 1/3/2011 to
30/6/2011 4 months 61,710,000 4,347,172,500
9 1/7/2011 to
15/1/2012
6 months 15
days 88,430,000 6,342,963,000
10 16/1/2012 to
30/4/2012
3 months 16
days 54,770,000 4,654,602,000
11 1/5/2012 to
30/9/2012 5 months 73,220,000 5,432,508,000
12 1/10/2012 to
31/3/2013 6 months 70,870,000 6,716,011,500
13 1/4/2013 to
30/9/2013 6 months 68,200,000 6,580,564,500
4.5 Transport Difficulty and Social Exclusion of Elderly in Bangkok
4.5.1 The awareness of changing in Thai population structure
In the past decades, Thai economy, social and culture have been considerably altered. Furthermore,
high technology in medical care and public health lead to the higher aged population in average (Life
Expectancy at Birth; Female 78.1, Male 71.1). Because of reductions in birth, death and pregnancy
rates, the percentage of young population is decreasing while the percentage of elderly population is
increasing as shown in Figure 4-21. The steady change of population structure economy, social and
culture is certainly a challenge for Thai government. In the past few years, Thai government pays
attention to improve a standard of living for elderly people. by policy determination, strategy planning
and regulation development to improve standard of Thai living of elderly are written below (Office of
the National Economic and Social Development Board, 2011).
49
• The 60th cabinet: Prime Minister Yingluck Shinnawatra’s ‘Economic security policy’ aims to
improve the standard of living for elderly people with incremental monthly income and ‘Social
and living quality policy’ aims to improve the quality of life for all groups of people
• ‘Older Persons Act 2003’ (revised 2010), which has come into force since 2004 contains
significant provisions specifically for the elderly such as the tax privilege for older persons and
children who take good care of their older parents, national management mechanism on older
persons and the Elderly Fund
• ‘The Second National Plan on Older Persons 2002 – 2021’ (revised in 2009) has been
implementing as a comprehensive strategic plan of 20 year-term for orientation of development
and promotion of the well-being of the elderly
4.5.2 Aging society and transport difficulty in Bangkok
In Southeast Asian countries such as Thailand, society will see dramatic growth in the percentage of
elderly population 60 years old and over, with estimated increase from 10% in 2008 to 21% 2020
(Suwanrada, 2009). This is also apparent for Bangkok, Thailand’s capital city. However, such rapid
development in the population has occurred without sufficient and appropriate public transportation
systems (Suparb and Ranjith, 2009).
In developing countries such as Thailand, the capital city has rapidly developed without sufficient
infrastructure, appropriate urban planning or adequate transportation to accommodate the needs of
senior citizens(Suparb and Ranjith, 2009). Consequently, transportation difficulties discourage the
elderly from leaving their homes to participate in society. This problem has become a key obstacle to
social inclusion from the perspective of urban planning in developing countries.
land use and road network conditions in Bangkok, it has been difficult to plan the public transport
network to cover the most areas in Bangkok. Although the average distance that people in Bangkok are
willing to walk for access to public transport stations is 797.6 meters, many areas in Bangkok are not
covered by radiuses of 800 meters from the bus stops or train stations (The Urban Change Agent,
2015). Unpredictable traffic conditions, unreliable travel times, and the lack of transit connection points
have been common problems for ground public transport (Khemapech and Kidbunjong, 2014). In
addition, long walking distances, crowded vehicles, and unsafe conditions while using services in
addition to difficult to use information systems make the use of public transport difficult for the elderly
(Kulachai, 2015).
Bangkok has a wide range of public transport options including buses, paratransit, trains, boats, and
rail. The most demand is for transit (bus and van) and metro (BTS and MRT) (Kulachai, 2015). Transit
operation consists of 7,525 buses and 5 ,3 1 6 vans servicing 459 routes (Bangkok Mass Transit
Authority). Besides, Bangkok Bus Rapid Transit (BRT) includes isolated rights of way and closed
stations, which have been operated for only 15.9 kilometers with 12 stations. BRT has failed in the
extension of services due to low demand, road capacity and land use in Bangkok (Wu and Pojani,
2016). For the Bangkok metro system, BTS and MRT are operated on 5 lines covering 112 kilometers.
Metro lines are connected with transit routes into the major ground public transport network in Bangkok
(Mass Rapid Transit Authority of Thailand).
50
Figure 4-21: Demographic situation
Recently, Bangkok has become a car-dominated city with 13,671,613 registered vehicles in 2014
(Kaewwongwattana et al., 2016). However, Bangkok as a city has sprawled along its radial axes with
inadequate loops and outer rings. The urban area contains many sections enclosed by main streets, but
not provided with connected distributor roads. Besides, the Bangkok road network lacks hierarchical
downscaling from major to minor roads (Wu and Pojani, 2016). The current situation has resulted in
not only severe traffic congestion, but also difficulty in public transport planning in Bangkok.
Nevertheless , people in Bangkok tend to travel by private vehicle because it offers more convenience
(Kaewwongwattana et al., 2016). Then again, driving is often more difficult for the elderly than
younger people. Further, travelling by public transportation might be inconvenient for elderly people in
various respects, such as accessibility to walkways, service information and safety. Therefore, both
elderly drivers and public transport users in Bangkok are more likely to experience difficulties in
transportation due to their age-related characteristics. Hence, unsatisfactory or insufficient
transportation systems might discourage elderly people from going out to engage in social activities,
leading to a lack of social participation and subsequent feelings of social exclusion.
51
4.5.3 Transportation policy to support elderly people in Bangkok
A separate special public transport system provided only for the elderly in Bangkok has yet to be
provided (Kulachai, 2015). In order to support the elderly, as well as disability and their use of public
transportation, various planned and transport policies have been created by government and ministry pf
transport as follow (OTP, 2018).
• The 5th National Plan on Empowerment of Persons with Disabilities (2017 – 2020)
• Second National Plan for Older Persons (2002-2021)
• The Provision of Equipment, Facilities or Services in the Buildings, Places, Vehicles and
Transportation Services to Ensure Accessibility and Usability by Persons with Disabilities B.E.
(since 2013)
• Strategies for Development of Facilities in Vehicles for Persons with Disabilities and Older
Persons (since 2016)
• Elderly Public Relations and Hearings (since 2017)
• Inclusive Transport Training for Auditor: ITTA and Inclusive Transport Training for Service:
ITTS (since 2017)
However, the improvement of facilities, such as low-floor buses with no steps between the curb and
entrances of passenger cabins, the improvement of access walkway conditions, travel assistance, and
simpler ticketing information systems, takes time to be completed and have not yet been developed.
Although the encouragement of social exclusion of disability persons has received high attention from
the government, In fact, the development of necessary support systems for specifically reducing the
feeling of social exclusion the elderly has not received the adequate attention. Further, the organization
responsible for providing transportation lacks understanding of the particular characteristics and needs
of the elderly. The issue is exacerbated by the fact that elderly groups have not publicly asserted their
needs or come out to require the provision of better public transport (Kulachai, 2015).
Although there are many groups in society with unfulfilled transportation needs, transportation (both
driving and public transportation) is still a common daily challenge for the elderly due to their mental,
physical, and financial conditions (DK Publishing, 2013). To participate in community events, visit
friends and relatives, perform administrative activities and attend medical appointments, the elderly
need affordable, safe and convenient transportation that allows them to travel independently in their
community (Connolly, 2012). The availability of adequate transportation could support the mobility
needs of elderly and prevent them from being isolated from society.
However, the sufficiency of transportation services depends on where the elderly live. The governments
of some countries have created strategies or programs to provide adequate transportation options for
seniors. Although local authorities do not provide sufficient transportation choices, they would ideally
offer assistance for finding alternative transportation for seniors (DK Publishing, 2013).
First, although the government aims to create the transport policy to support low-mobility group in
Bangkok for transportation needs, the effect of those soft-infrastructure policies on reduction of feeling
of social exclusion of low-income and elderly groups have been rarely evaluate. Therefore, this study
focuses on the evaluation of the effect of transport policy on the feeling of social exclusion caused by
transport difficulty of these two groups.
52
CHAPTER 5 FRAMEWORK OF THE STUDY
5.1 Framework of the Study
This chapter explains how to design conduct the framework of the study. As mentioned earlier, the aim
of this study is to address the issues of feelings of social exclusion caused by transport difficulty of two
groups of low mobility, which are low-income and elderly people in Bangkok. The framework of the
study consists of 1) review process (concept of social exclusion, literatures and current transportation
situation of Bangkok), 2) Methodology, study and result (the experimental design, data collection,
analysis), 3) Discussion (conclusion, implication, contribution and limitation) as written in Figure 5-1.
After reviewing previous studies and the current situation in Bangkok, the importance and rational of
the study were found, and subsequently framework of how to conduct the study, conclude the overall
study results and recommend the policy implication based on those results to the Thai government, were
designed. The explanations of each item of the framework is described below.
Figure 5-1: The framework of the study
53
5.2 Study Design
5.2.1 The definition of social exclusion in this study
From Figure 4-1, based in the review process, the concept of social exclusion and its relationship with
the transport difficulty was understood, and the scope of social exclusion of the study was clearly
defined separately from the concept of social segregation. Social exclusion has very wide definition and
the process. In this study, the definition of social exclusion refers to the feeling socially excluded of
people living in the city, which is different between low income and elderly group. The definition of
low income people is mainly focused on feeling excluded from the social resourced and opportunities,
such as job opportunities and social services. However, for elderly people, they tended to focus on their
relationship with other people in their community, how important they are for the society, and how to
enjoy their life, rather than seeking the job or educational opportunities in their late live, according to
the previous study. Thus, the definition of social exclusion of elderly in this study is also focused on
the feeling isolated, unimportant, not being part of society, and lack of social relationship.
In terms of the process of social exclusion, there are various situations that can cause the feeling of
social exclusion of people living in the city, such as poverty, social discrimination and inaccessibility,
but this study focuses on the process that a person could not reasonably access or participate in
mainstream social activities outside their homes or neighborhood areas, services and opportunities
because of transport difficulty, which is also different between low income and elderly people. In this
study, transport difficulty of Bangkok low income people is highlighted on their unaffordability to the
fare of transportation, and those of Bangkok elderly people is mainly focused on unsatisfactory
transportation caused by incompletely developed transport service performance, limited physical
condition and living condition (inadequate income of elderly is also included).
Therefore, the title ‘Transportation Policy for the Reduction of Social Exclusion of Low-Income and
Elderly People in Bangkok’ of this dissertation refers to how the proposed transport policy in this study
can solve the issue of inaccessibility of low income and elderly people, and subsequently reduce the
degree of feeling social exclusion on them according to the mentioned definition. This study also
applied an alternative method which was slightly different from the previous methods to measure the
feelings of social exclusion for each group of low mobility that will be explained in each chapter (6 to
8).
5.2.2 The originality of the study
In the academic aspect, although several studies have evaluated the degree of social participation by
measuring a person’s actual travel ability, such as trip frequency and access to public activity spaces
(Church et al., 2000; Ihlanfeldt, 1993; Matthews et al., 2003a; Schmöcker et al., 2006), the desired
level of social participation might not be precisely evaluated by relying exclusively on that person’s
existing transportation. Another possible way of identifying whether elderly people have mobility
deficits is to observe if people would prefer to travel more frequently for increased participation in
social activities and services. Therefore, this study introduces the concept of measuring the gap between
desired and existing trip frequencies to calculate the amount of social participation deficits. In addition,
current transport ability and level of service is difference from the actual feeling of social exclusion.
54
Therefore, this study measures and quantifies the actual degree of feelings of social exclusion of people
by relying on psychological indicators adapted from the standard questionnaires for ‘Quality of Life for
the Elderly’ (Bilotta et al., 2011; Raphael et al., 1995), which have been rarely applied before.
In terms of practical aspect, the issue of transport difficulty-related social exclusion has been widely
studied in developed countries, but this topic gets only little attention from the government in
developing country, especially in Bangkok, the capital city of Thailand. Thus, this study selected low
mobility living in Bangkok as the focused group, especially low income and elderly. In addition, the
evaluated policy and proposed transport policy in this dissertation have not been focused in the low
mobility transportation support plan of the government before as described in the next section.
5.2.3 The reason why low-income and elderly group were focused
The review of previous works showed that the issue of social exclusion of low mobility caused by
transport difficulty has been widely studied and receive high attention from the government and policy
maker in the global north countries, but there is still lacking focus of this issue in global south countries.
Thus, the study chose Thailand, one of global south country, as the study area. Therefore, this study can
become the new focused aspect for the Thai government to address this issue.
In the fact of Thailand, the encouragement of social inclusion of disability group has already received
high attention from the government. Although the organizations under the ministry of transport have
created various transport policy to support low income and elderly for travel needs, the research on the
aspect of transport policy to reduce the degree of feeling social exclusion for those groups has been
rarely focused in Thailand. To response to the travel needs to participate in social activities and access
social services that can reduce the feeling of social exclusion of those group, the study to find the proper
policy implication should be conducted.
A) The study on low income group
There are many general policies to support low income, such as policies to increase income, welfare
and job opportunities of them. However, if we focus of transportation policy, a possible policy is the
make transportation more affordable for low income, which is transport subsidy policy, but the amount
of subsidy money should not be distributed directly to low income people directly because that amount
of money can be spent for other purposes.
Although the government created public transport policies to support low-income (free bus and train
policy), the evaluation in terms of the increase in degree of social participation of those low income and
the distribution of the subsidy policy to target group have been rarely focused. If the government wants
to reduce the degree of feeling social exclusion of those low income by this policy, it is importance to
evaluate the effect of the policy on reducing social exclusion. Thus, this research conducts the study on
the evaluation of public transport subsidy in the aspect of the reduction of degree of social exclusion of
low income in the Chapter 6.
In this Chapter, a possible way to solve the problem of unaffordability of low-income group is to make
transportation cheaper. Therefore, this study took the example of free train policy to examines the
increase in trip frequency to participate in social activities of those Bangkok low income before and
55
after using free train, as well as the feeling of social exclusion (the further detail will be explained in
Chapter 6).
However, although there are various criteria to define who is low-income, this study bases on the criteria
of the lowest salary per day of Department of Labor protection and welfare. Since 2014, the government
of Thailand has defined the minimum wage rate, 22,292 JPY per month (Ministry of Labour, 2014).
Therefore, any sample with monthly income lower than 22,292 JPY was considered to be low-income
person.
B) The study on elderly group
According to the latest plan the Ministry of Transport (MOT) and Office of Transport and Traffic Policy
and Planning (OTP) to support elderly group for transportation, the focus of the program is to construct
the new infrastructure and provide the new public transport vehicles. However, which aspect of
transport service was unsatisfied and caused the feeling of social exclusion by elderly has been rarely
investigated before. Thus, this study aims to investigate the social exclusion of elderly caused by current
transportation difficulty and propose the policy implication to improve those unsatisfied point in order
to reduce the degree of feeling social exclusion of them.
The government has been trying to improve the level of service (LOS) of transport systems in Bangkok
as a universal policy with relatively no specific target group. However, there is no specific evaluation
in terms of the reduction of feeling of social exclusion of elderly people related the improvement of
LOS yet. In addition, there are various aspects of LOS of many transportation modes that should be
improved. Which LOS aspect and which mode should get the priority to be improved first in order to
efficiently reduce the degree of feeling social exclusion of elderly, has been rarely focused. Therefore,
it is importance to evaluate the effect of each aspect of the improvement of LOS on decreasing the
degree of social exclusion of elderly as in Chapter 7.
The study of Chapter 6 measures the feeling of social exclusion related to degree of satisfaction with
ground transportation modes that elderly usually used for travelling in the city to obtain how severe of
the current social exclusion-related transport difficulty of elderly. This part also examines the mobility
deficit of elderly people, which is represented by the gap between the number of desired and existing
trip frequency to participate in various kind of social activity. The efficient way to improve the LOS to
reduce the gap in number of trips and the feeling of social exclusion of them will be also discussed (the
further detail will be explained in Chapter 7).
However, the study of elderly could not be finished with in one chapter because there were still elderly,
living far from the public transport station, cannot both access the public transport service and call the
paratransit (e.g. taxi and tuk tuk). Although the government implements the policy to improve the
facility and LOS of public transport service, this group of elderly may not receive the benefit of that
policy. In addition, the government nowadays created various door-to-door service policies to support
travel needs of elderly, such as elderly taxi, demand responsive transit (DRT) targeting elderly people,
but that services have not been covered those areas in Bangkok yet due to the lack of budget, service
vehicles and drivers. Therefore, this section proposes the concept of utilizing the existing community
human resources by establishing an elderly carpool support service by neighborhood driver in those
areas that public transports are difficult to access that will be studied in Chapter 8.
56
Chapter 8 evaluates the increase in trip frequency and the decrease in degree of feeling social exclusion
of elderly by introducing this carpool support service. The data collection was mainly conducted in the
area far from public transportation in Bangkok. The appropriate acceptable service price for elderly
(who use the service) and neighborhood (who drive to support elderly) was also estimated in this section
(the further detail will be explained in Chapter 8).
It has to be noted that the analysis bases on only the data collected from Bangkok elderly (who is 60 or
over 60 years old). Only the existing ground public transportation service and condition at when data
was collected are focused in this study. For the evaluation of elderly carpool support program, it should
be noted that the scope focuses only on those only willing to join the system, while other aspects such
as calling system, law, regulation and taxation are not studied in the study.
5.2.3 The design of discussion of policy implication base on the overall result of the study
After the analyses of the proposed transport policies in Chapter 6 to 8, the effect, target group, and
implementation of each policy for reducing the feeling of social exclusion of low-income and elderly
groups will be found. This study would try to recommend the transport support policy implication that
has rarely focus in the national plan of the MOT and OTP. Thus, the government can utilize them for
solving the problem of social exclusion-related transportation difficulty of low income and elderly in
the future. The results of all chapters will be used together to guide for the direction of transport policy
implication to promote social inclusion, which will be proposed in Chapter 9.
57
CHAPTER 6 PUBLIC TRANSPORT SUBSIDY TO REDUCE SOCIAL
EXCLUSION OF BANGKOK LOW INCOME
6.1 Methodology
This section of the study took the example of free train policy, a subsidy policy launched by the Thai
government from August 1, 2008, in order to examine to evaluate the benefit in terms of travel cost
saving of public transport subsidy on reducing the feeling of social exclusion of low-income people in
Bangkok. (State Railway of Thailand, 2014).
The assumption of this section is that the advantage of the reduction of travel cost by using free train
would encourage Bangkok low-income user to travel more frequently to participate in more social
activity, leading to the less feeling of social exclusion of them. Therefore, the aim of this part is to assess
whether or not free train policy could encourage low-income people to travel more frequently for
participating in more social activities. In addition, this part seeks to confirm if low-income groups
receive the benefit from this subsidy policy in terms of ability to make more trip and feel less social
exclusion, as well as to estimate error of inclusion from the ratio of the number of non-target group
users (non-low-income users) to the total number of free train users. How to conduct the questionnaire
survey and analyzed the data is written below.
6.1.1 Data collection and the measurement of variables
The analysis used the previous data collected randomly from public train passengers (including all class
of seats) by intercept interview approach, using Retro-perspective questions in parts of the questionnaire
to recall the respondent’s memory before and after using the free train. In January 2013, 392 samples
were collected in the area around the hub of rail operation, including the major train stations in Bangkok
Metropolitan Area: Hua Lampong, Sam Sen, Bang Sue Junction, Bang Khen, Lak Si, Don Muang,
Makkasan, and Thonburi and Wongwian Yai stations (as shown in Figure 6-1). Commuting respondent
would be skipped, because they travelled in same frequency due to their trip purposes.
The data collected from train users, such as trip frequencies to go out for participating in social activities,
travel cost, travel time and travel distance included not only the information of their major mode, but
also those of their access and egress modes. For checking the accuracy, all data were examined by
comparing with the Geographic Information System (GIS). Travel cost, travel time and travel distance
reported by all respondents were checked with fare, fuel cost, speed and distance databases. For
reliability of socio-demographic data, all samples were reviewed before the analysis. Individual
monthly income must correspond with total household income to the number of members in household.
In addition, the respondents were also asked to rate the degree of social exclusion at the situation before
and after using free train. In this case, the social exclusion refers to the situation that respondents feel
not being able to participate in social opportunities, services and activities rated by Linkert scale from
1 (not feel at all) to 5 (extremely feel). Finally, the full questionnaire (both English and Thai version)
and survey pictures are shown in Appendix A.
58
Figure 6-1: Survey locations
In the analysis, If the respondent reported any controlled situation, this sample was then ignored form
the analysis. There are other controlled situations that made the users shift their ground transportation
modes to use free train described below.
- Respondents moved their resident location, working place, or study area
- Respondents faced the financial problem
- The operations of any other ground public transports were suspended
- The prices of any other ground public transports were increased
- Other reasons, nor related the free travel cost of free train
6.1.2 To investigate the relationship between reduction of travel cost and increased trip
frequency
To examine if the free public train users travelled more frequently for participating more social activities
due to travel cost saving, the Binary Logistic Model (BLM) was applied for the statistical analyses. In
this study, the choices were coded into 0 and 1 as dependent variable (yi) that is shown in title bellow.
0 if the respondent i travelled in more frequently after using free train
yi =
1 if the respondent i did not travel in more frequently after using free train
Independent variables were chosen from theoretical variable were divided into two groups as follow:
1) Trip information included trip frequency, travel cost, travel time and travel distance (before and after
using free public train), and other public transportations that respondents had used before, and amount
of travel cost saving after using free train. This also included information from origin to train station
and from train station to destination and 2) Socio-demographic variables included gender, age,
educational level, disability or patient status, vehicle ownership, employment status, occupation and
their income. The probability and the utility function of the binary logit model are expressed as
Equation 6-1 and 6-2, respectively (Ben-Akiva and Lerman, 1985). The framework of the binary
logistic regression is shown in Figure 6-2.
59
𝑷𝒊 =𝒆𝑼𝒊
𝟏 + 𝒆𝑼𝒊 (6-1)
𝑼𝒊 = 𝜷𝟎 + 𝜷𝟏𝑿𝒊𝟏 + 𝜷𝟐𝑿𝒊𝟐 + ⋯ + 𝜷𝒏𝑿𝒊𝒏 + 𝜺 (6-2)
Where;
Pi : the probability that the event that personi travelled more frequently to
participate in more social activities
Ui : utility function for personi,
Xi1, Xi2 ,…, Xin : the value of each independent variable,
β0 : y-intercept or the expected value of Ui when all xi are zero,
β1, β2,…,βn : coefficients for Xi1, Xi2,…, Xik, receptively, and
ε : error value that is expected to be zero.
6.1.3 To estimate the error of inclusion of the subsidy
It is possible that non-low-income people (non-target group) would also still use free public train for
their trips, so it is important to clarify if low-come (target group) received this benefit from free train in
terms of ability to make more trip and feel less socially excluded rather than non-low income did. Thus,
the degree of distribution of this subsidy policy to the target group should be also estimated
Recently, the average of income of Bangkok population is 157223 JPY per month. To identify the error
of inclusion (unintended benefit), the criteria to judge who is low-income who should receive the benefit
from the subsidy of Ministry of Labor, 2014 was applied. The person who earns less than 22,292 JPY
per month is considered to be low income person (Ministry of Labour, 2014). Equation (6-3) was
applied calculate the proportion of un-intended benefit of free train policy.
𝐓𝐡𝐞 𝐞𝐫𝐫𝐨𝐫 𝐨𝐟 𝐢𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧
= 𝐓𝐡𝐞 𝐧𝐮𝐦𝐛𝐞𝐫 𝐨𝐟 𝐧𝐨𝐧 𝐥𝐨𝐰 𝐢𝐧𝐜𝐨𝐦𝐞 𝐟𝐫𝐞𝐞 𝐭𝐫𝐚𝐢𝐧 𝐮𝐬𝐞𝐫𝐬
𝐓𝐡𝐞 𝐧𝐮𝐦𝐛𝐞𝐫 𝐨𝐟 𝐭𝐨𝐭𝐚𝐥 𝐟𝐫𝐞𝐞 𝐭𝐫𝐚𝐢𝐧 𝐮𝐬𝐞𝐫𝐬 × 𝟏𝟎𝟎
(6-3)
Figure 6-2: The structure of the analyses
60
6.2 Data
6.2.1 Descriptive statistic
Descriptive statistics of frequency and continuous data are shown in Table 6-1 and Table 6-2,
respectively. It was found that 128 out of 392, including both former and new train users, traveled more
frequently to participate in more social activities after using free service. Therefore, it was a good sign
for free train policy because of this growing in social inclusion. Large proportion of traveler travelled
for visiting their hometown (61.99%). For new user, most passengers shifted from coach (intercity bus;
15.31%) and van (10.97%). One hundred and twenty-eight of those who travel more frequently traveled
for visiting hometown (20.15%), visiting friend or family (6.89%), business (2.04%) and recreation
(3.57%).
From Table 6-1 and 6-2, a half of respondents was male and other fifty percent was female. The average
age of the respondent was 36.99 years old. Respondents had various educational level and did several
occupations. Large portion of respondents owned their own vehicle, including private car (52.81) and
motorcycle (51.53). The average monthly income of them was 24978.59 JPY. From Table 6-2, before
using free train, the average of travel cost of respondents was 824.41 JPY per trip. After using free train,
the average of travel cost saving was 621.83 JPY per trip (saving 63.47%), which was divided into
317.81 JPY per trip (saving 55.84%) and 1347.36 JPY per trip (saving 82.56%) for existing user and
new user, respectively. It showed that people who shifted from one to another mode could save more
travel cost than those of the former users.
6.2.2 Increase in trip frequency and decrease in degree of feeling social exclusion
The data indicated that many respondents travel more frequently to participate in more social activities
as shown in Figure 6-3. Most of them travel more frequently to visit their home town and visiting their
friends or relatives. In this study, the amount of increased in trip frequency after using free train was
compared between low-income (monthly income less than 22292 JPY) and non-low-income samples
as shown in Table 6-3 and Figure 6-4. It is obviously seen that the increases in trip frequency of low-
income free train users were much larger than those of non-low-income users. It has to be noted that,
the average increase of trip frequency of low-income people was just 4.30 trip per year which may be
considered to be low because those trip purposes were not commuting or fixed schedule trip that they
generally travel. However, these trip purposes were extra trip purposes that they did not generally make,
such as visiting hometown as mentioned before.
In addition, the feelings of social exclusion before and after free train of low-income and non-low-
income users were also measure by Linkert scale (1: not feel at all to 5: extremely feel) as shown in
Figure 6-5 (P-value is according to statistical T-test). It seems that low-income people already had
higher degree of social exclusion (3.86) rather than those of non-low income (2.75). However, it is
apparently seen that low-income users had significant decrease of degree of feeling of social exclusion
rather than those of non-low-income users. This decrease of degree of social exclusion had significant
Pearson correlation with the increase of trip frequency (time/year) by 0.88 with 0.77 coefficient. Until
this point, it can be implied that low-income users tended to receive the benefit of free train in terms of
increase of trip frequency and decrease of social exclusion rather than those of non-low-income users.
61
Table 6-1: Descriptive statistics of frequency data (392 respondents)
Description Frequency Proportion (%)
Trip purpose
Visiting hometown 243 61.99
Visiting friend or family 77 19.64
Business 37 9.44
Recreation 35 8.93
Induced demand (Social inclusion)
All free train user who travelled more frequently 128 32.65
For visiting hometown 79 20.15
For visiting friend or family 27 6.89
For business 8 2.04
For recreation 14 3.57
Shifted demand (New free train users)
All new user 112 28.57
Shifted from coach (intercity bus) 60 15.31
Shifted from van 43 10.97
Shifted from other modes 9 2.30
Socio-demographic
Male 196 50.00
Female 196 50.00
Person with difficulty to travel themselves due health condition 117 29.85
Educational level
Non-study 24 6.12
Primary school 56 14.29
Secondary school 64 16.33
High school 82 20.92
Vocational diploma 78 19.90
Bachelor degree or higher 88 22.45
Employment status
Total employed 290 73.98
Governmental employee 52 13.27
Private company employee 133 33.93
Business owner or merchant 55 14.03
Agriculturist 23 5.87
Unskilled laborer 27 6.89
Vehicle ownership
Vehicle owner 297 75.77
Motorcycle owner 202 51.53
Car owner 207 52.81
Average household income (JPY per person per month)
New user with income ≥ 22292 37 9.44
New user with income < 22292 75 19.13
Existing user with income ≥ 22292 121 30.87
Existing user with income < 22292 159 40.56
Free bus policy
Using free bus to access of egress train station by free bus 48 12.24
Who travelled more frequently (using free bus) 29 7.40
62
Table 6-2: Descriptive statistics of continuous data
Description Mean S.D.
Trip information (One-way trip)
Total travel cost before using free train (JPY) 979.662 930.672
Total travel cost after using free train (JPY) 357.834 324.4035
Total travel cost saving after using free train (JPY) 621.828 571.0095
• Total travel cost saving of existing user 331.614 317.7105
• Total travel cost saving of new user 1347.363 963.3435
Total travel cost saving rate after using free train (%) 63.47 23.93
• Total travel cost saving rate of existing user 55.84 22.77
• Total travel cost saving rate of new user 82.56 16.88
Total travel time after using free train (minute) 516.45 310.20
Total travel distance after using free train (kilometer) 428.99 292.17
Socio-demographic
Age (year) 36.99 15.54
Household average income (Baht/person/month) 7240.17 4993.05
Distance from house to station (meter) 1453.41 754.65
Figure 6-3: The number of free train users travelling more frequently
Table 6-3: Increase of trip frequencies
Activity Type
Low income Non-low income
Previous
trip freq.
Existing
trip freq. Dif.
P-value
(T-test)
Previous
trip freq.
Existing
trip freq. Dif.
P-value
(T-test)
Visiting
Hometown 0.52 1.23 0.71 0.001 1.01 1.11 0.10 0.341
Visiting Friend
or relative 2.11 7.85 5.74 0.001 5.44 6.15 0.71 0.097
Part time
business 11.22 12.11 0.89 0.124 6.23 6.77 0.54 0.451
Leisure 6.54 14.35 7.81 0.001 8.71 9.44 0.73 0.121
14
8
27
79
21
29
50
164
0 50 100 150 200 250
Leisure
Part time business
Visiting friends or relatives
Visiting hometown
Made more trips Did not make more trips
63
Figure 6-4: Increase of trip frequencies
Figure 6-5: Decrease in degree of social
6.2.3 Unintended benefit
In the case of Thailand, the person who earns less than 22292 Baht per month is considered to be low
income person (Ministry of Labor, 2014). From Table 3, the average monthly income of free train user
was 7,240 Baht which is slightly higher than the minimum wage rate. According to Table 6-1 and
Equation (6-3), 158 of 392 total free train users were non-low-income group; therefore, the percent of
unintended benefit was 40.31%. The portion of non-low income of new user was 33.04% (37 out of
112) which is less than 43.21% (121 out of 280) of existing user.
0
2
4
6
8
10
Low income users Non-low income users
Tim
e per
yea
r
Before using free train After using free train
P-value
0.001
0
1
2
3
4
5
Low-income users Non-low income users
Before After
P-value 0.001 P-value
0.324
P-value 0.553
64
6.3 Statistical Analysis
To examine if free train user travelled more frequently for participating social activities and to identify
which group of Thai citizen received the benefit in terms of social inclusion, econometric model using
the binary logistic regression, including the list of variables shown in Table 6-4, was conducted. Four
binary logit models (BLM as shown in Table 6-5) were developed as below.
1. BLM1 included category of new users, travel cost saving, travel cost saving rate, age
(continuous variable), category of occupation, household income (continuous variable) and
other same variables.
2. BLM2 included category of new users, travel cost saving, travel cost saving rate, age range
(dummy variable), category of occupation, household income range (dummy variable) and
other same variables.
3. BLM3 included group of new users, travel cost saving, travel cost saving rate, age range
(dummy variable), group of occupation into employment status, household income range
(dummy variable) and other same variables.
4. BLM4 included group of new users, travel cost saving (separation between new and former
train users: interaction variable), travel cost saving rate (separation between new and former
train users: interaction variable), age range (dummy variable), group of occupation into
employment status, household income range (dummy variable) and other same variables.
In model fitting, independent variables from all categories (trip information, gender, age, educational
level, occupation and income) were added to the model one by one. When each parameter was added
or dropped, log-likelihood ratio test was conducted for testing the significant difference between
unrestricted and restricted models. The models were tested recursively until factors from all categories
gave the highest Adjusted Pseudo R² and Log-likelihood estimation.
According to Table 6-5, four Binary Logit Models were developed from BLM1 to BLM4. The first
model (BLM1) was estimated from 23 parameters contained in data set, (see Table 6-5, showing the p-
value of the parameters in brackets). In the BLM1, continuous variable; age (AGE) and average
household income per person per month (AVR_HH_INC) were included. Experimentally, AGE and
AVR_HH_INC were divided in levels as dummy variables in BLM2, which gave higher Adjusted
Pseudo R² (0.283). Additionally, dummy variables of new user who shifted from coach
(NEW_COACH), van (NEW_VAN) and other modes (NEW_OTHERS) were grouped in to all new
free train users that shifted from every former transportation (NEW_USER), and all of occupation
variables (OCUP0 to OCUP5) were experimentally grouped into employment status (EMP) as shown
in BLM3. Finally, BLM3 gave higher Adjusted Pseudo R² (0.210).
In BLM4, interactive variables were developed. The result indicates that new user (NEW_USER) would
save more travel cost than existing train user (99% confident level from statistical T-test). Therefore,
the effects of travel cost saving (D_COST) and travel cost saving rate (D_COST_RATE) may not be
equal between new and former public train users. Hence, interactive variables [D_COST*NEW_USER,
D_COST*(1- NEW_USER), D_COST_RATE*NEW_USER, and D_COST_RATE*(1-
NEW_USER)] were developed in BLM4. Which variables that were multiplied by “NEW_USER” were
for new train user; on the other hand, which variables that were multiplied by “(1-NEW_USER)” were
for existing train user. The coefficients of D_COST and D_COST_RATE of new users and existing
65
users were quite different. It was implied that the effects of total travel cost saving were slightly not
equal between new users and former users.
Table 6-4: Descriptions of variables
Variable Types Description
Dependent variable
SOCIAL_IN Dummy 1 if respondent travelled more frequently for participating more social
activities after using free train, 0 is otherwise
Trip characteristic variable
OBJ1 Dummy 1 if respondent travelled to visit hometown, 0 is otherwise
OBJ2 Dummy 1 if respondent travelled to visit friend or family, 0 is otherwise
OBJ3 Dummy 1 if respondent travelled for business, 0 is otherwise
OBJ4 Dummy 1 if respondent travelled for leisure, hobby or shopping, 0 is otherwise
D_COST Continuous A different amount of travel cost of free train user after using free
train (JPY)
D_COST
*(NEW_USER)
Continuous
(interactive)
A different amount of travel cost of new train user after using free
train (JPY)
D_COST
*(1-NEW_USER)
Continuous
(interactive)
A different amount of travel cost of existing train user after using free
train (JPY)
D_COST_RATE Continuous A different amount of total travel cost of free train user after using
free train to total travel cost before using free train (%)
D_COST_RATE
*(NEW_USER)
Continuous
(interactive)
A different amount of total travel cost of new user after using free
train to total travel cost before using free train (%)
D_COST_RATE
*(1-NEW_USER)
Continuous
(interactive)
A different amount of total travel cost of existing train user after using
free train to total travel cost before using free train (%)
T_TIME Continuous Total travel time of trips (minute)
D_TIME_OF_NEW Continuous A different travel time of new user after using free train (minute)
COST_A Continuous Travel cost to access train stations: both from origin to initial station
and from terminal station to the destination (JPY)
NEW_USER Dummy 1 if respondent was new free train who shifted from other modes, 0 if
respondent was existing train user
NEW_COACH Dummy 1 if respondent shifted from coach to free train, 0 is otherwise
NEW_VAN Dummy 1 if respondent shifted from van to free train, 0 is otherwise
NEW_OTHERS Dummy 1 if respondents shifted from other transportation (except coach and
van) to free train, 0 is otherwise
Socio-demographic variable
GEN Dummy 1 for male, 0 for female
AGE Continuous Age of respondents (year)
AGE≤25 Dummy 1 if respondent was less than or equal to 25 years old, 0 is otherwise
EDU2 Dummy 1 if respondent graduated from high school or lower, 0 is otherwise
DIFF Dummy 1 if respondent was difficult to move themselves due to health
condition, 0 is otherwise
VEH_OWN Dummy 1 if respondent owned private vehicle at least 1, 0 is otherwise
EMP Dummy 1 if respondent was employed, 0 is otherwise
OCUP0 Dummy 1 if respondent had no job, 0 is otherwise
OCUP1 Dummy 1 if respondent was governmental employee, 0 is otherwise
OCUP2 Dummy 1 if respondent was private company employee, 0 is otherwise
OCUP3 Dummy 1 if respondent was business owner or merchant, 0 is otherwise
OCUP4 Dummy 1 if respondent was agriculturist, 0 is otherwise
OCUP5 Dummy 1 if respondent was unskilled labourer, 0 is otherwise
AVR_HH_INC Continuous Average household monthly income per person (Baht/person/month)
AVR_HH_INC≤22292 Dummy 1 if average household income per person per month is less than or
equal to 22292 Baht/person/month, 0 is otherwise
66
Table 6-5: Estimated parameters of the Binary Logit Model (BLM)
Variable BLM1 BLM2 BLM3 BLM4
NEW_COACH -0.023(-0.877) -0.259(0.471) NA NA
NEW_VAN 0.342(0.498) 0.175(0.695) NA NA
NEW_OTHERS -0.721(0.457) -1.011(0.336) NA NA
NEW_USER NA NA -0.007(0.971) 3.650(0.003)***
OBJ1 0.071(0.698) 0.026(0.975) 0.081(0.604) 0.054(0.742)
OBJ2 (Dropped) (Dropped) (Dropped) (Dropped)
OBJ3 -0.571(0.356) -0.687(0.193) -0.753(0.121) -0.771(0.198)
OBJ4 0.512(0.632) 0.401(0.887) 0.513(0.745) 0.598(0.745)
D_COST 0.001(0.098)* 0.001(0.045)* 0.001(0.075)* NA
D_COST*(NEW_USER) NA NA NA 0.001(0.089)*
D_COST*(1-NEW_USER) NA NA NA 0.001(0.001)***
D_COST_RATE 0.061(0.001)*** 0.064(0.001)*** 0.087(0.001)*** NA
D_COST_RATE*(NEW_USER) NA NA NA 0.024(0.087)*
D_COST_RATE*(1-
NEW_USER) NA NA NA 0.061(0.001)***
T_TIME -0.001(0.284) -0.001(0.345) -0.001(0.267) -0.001(0.332)
D_TIME_OF_NEW 0.004(0.261) 0.004(0.275) 0.004(0.240) 0.006(0.145)
COST_A 0.003(0.049)** 0.004(0.098)* 0.004(0.066)* 0.010(0.054)*
GEN 0.375(0.456) 0.339(0.782) 0.458(0.436) 0.349(0.391)
AGE -0.025(0.031)** NA NA NA
AGE≤25 NA 0.686(0.045)** 0.699(0.078)* 0.548(0.097)*
EDU2 0.586(0.117) 0.256(0.489) 0.198(0.524) 0.125(0.753)
DIFF 0.346(0.275) 0.223(0.499) 0.192(0.553) 0.172(0.589)
VEH_OWN 0.304(0.289) 0.381(0.189) 0.415(0.158) 0.421(0.159)
EMP NA NA 0.592(0.082)* 0.599(0.087)*
OCUP0 -0.286(0.410) -0.623(0.112) NA NA
OCUP1 -0.069(0.863) -0.149(0.699) NA NA
OCUP2 (Dropped) (Dropped) NA NA
OCUP3 0.552(0.189) 0.307(0.445) NA NA
OCUP4 0.154(0.777) 0.049(0.807) NA NA
OCUP5 -0.376(0.549) -0.349(0.534) NA NA
AVR_HH_INC -0.001(0.099)* NA NA NA
AVR_HH_INC≤22292 NA 1.256(0.001)*** 1.356(0.001)*** 1.375(0.001)***
(Constant) -4.241(0.000) -6.479(0.000) -7.127(0.000) -7.484(0.000)
Observations 392 392 392 392
Pseudo R² 0.232 0.245 0.262 0.295
Adjusted Pseudo R² 0.168 0.182 0.198 0.214
Log-likelihood -195.878 -192.458 -189.787 -184.632
AIC 435.756 428.916 411.574 425.264
BIC 523.124 516.284 475.114 536.459
% Correct Prediction 76.132 74.212 75.451 78.421
NA Not applicable; *** Significant at 1% level; ** Significant at 5% level; * Significant at 10% level.
According to Table 6-5, four Binary Logit Models were developed from BLM1 to BLM4. The first
model (BLM1) was estimated from 23 parameters contained in data set, (see Table 6-5, showing the p-
value of the parameters in brackets). In the BLM1, continuous variable; age (AGE) and average
household income per person per month (AVR_HH_INC) were included. Experimentally, AGE and
AVR_HH_INC were divided in levels as dummy variables in BLM2, which gave higher Adjusted
Pseudo R² (0.283). Additionally, dummy variables of new user who shifted from coach
(NEW_COACH), van (NEW_VAN) and other modes (NEW_OTHERS) were grouped in to all new
free train users that shifted from every former transportation (NEW_USER), and all of occupation
variables (OCUP0 to OCUP5) were experimentally grouped into employment status (EMP) as shown
in BLM3. Finally, BLM3 gave higher Adjusted Pseudo R² (0.210).
67
In BLM4, interactive variables were developed. The result indicates that new user (NEW_USER) would
save more travel cost than existing train user (99% confident level from statistical T-test). Therefore,
the effects of travel cost saving (D_COST) and travel cost saving rate (D_COST_RATE) may not be
equal between new and former public train users. Hence, interactive variables [D_COST*NEW_USER,
D_COST*(1- NEW_USER), D_COST_RATE*NEW_USER, and D_COST_RATE*(1-
NEW_USER)] were developed in BLM4. Which variables that were multiplied by “NEW_USER” were
for new train user; on the other hand, which variables that were multiplied by “(1-NEW_USER)” were
for existing train user. The coefficients of D_COST and D_COST_RATE of new users and existing
users were quite different. It was implied that the effects of total travel cost saving were slightly not
equal between new users and former users.
To eliminate biases, if each variable significantly related to the event of social inclusion in only some
models, this variable would not be considered as a significant variable. The robust significant variable
was only which variable that had significant relationships with the dependent variable in all models.
The acceptable significant variable must have at least 90% confident level.
6.4 Discussion
6.4.1 Influenced factors from the model
According to Table 6-1, income level (AVR_HH_INC) and who has average household income less
than 22292 JPY per person per month (AVR_HH_INC≤22292), which was considered as low-income
level comparing with Thai minimum wage rate according to Ministry of Labour (2014), had positive
correlation with the event of social inclusion (SOCIAL_IN). The magnitude of coefficient of
AVR_HH_INC≤22292 was considered to be large (around 1.256 to 1.375). Hence, it appeared that low-
income group tended to receive the benefit in terms of ability to travel more frequently.
From Table 6-4, the age of free train users (AGE) had a significant negative relationship with
SOCIAL_IN. It was implied that younger travelers tended to travel more frequently after using free
service, especially who was less than or equal to 25 years old (AGE≤25; the coefficients were around
0.548 to 0.686). The analysis of income level was corresponding with the result of age. Typically,
income level goes along with age. Most young Thai population (who is less than or equal to 25 years
old; AGE≤25) had not earned high level income yet because most of them tended to be new employees
or even still under study in school or university. In addition, who were employed (EMP) tended to travel
to join more social activity after using free train according to the significant positive relationship. Its
magnitudes were quite large (around 0.592 to 0.599). It was implied that Thai worker or labor received
the benefit in terms of social inclusion, especially low income.
In the context of travel cost saving, the travel cost to access train station (COST_A) of those who travel
more frequently were quite expensive, but their total travel cost decreased after using free service.
Travel cost saving (D_COST) and travel cost saving rate (D_COST_RATE) after using free train had
positive relationships with SOCIAL_IN. It appeared that the advantage of travel cost saving persuaded
Bangkok people to travel more frequently for joining society. The effect of travel cost saving was
obvious in both existing and new public train users according to the significant positive relationships
68
of D_COST*(NEW_USER), D_COST*(1-NEW_USER), D_COST_RATE*(NEW_USER), and
D_COST_RATE*(1-NEW_USER).
However, the magnitudes of the coefficients of travel cost saving were quite not so large (around 0.001
for D_COST, and 0.061 to 0.087 for D_COST_RATE*(1-NEW_USER). It may indicate that the effect
of travel cost saving was not so obvious in overall user. The reason was that typical fare of third class
seat is likely to be cheap, so the free service may not influence non-low-income person so much. Also,
there was 40.31% of non-low-income (unintended benefit) who was still using free service.
Nevertheless, these amounts of travel cost saving would more impress them for making more trips to
join society if the focusing was given to low-income (AVR_HH_INC≤22292) group. Therefore, it could
be implied that the benefit in terms of promoting social activity was efficiently distributed to target
group (low-income).
6.4.2 To improve free train policy
The result indicated that low income users tended to receive the benefit in terms of more ability to travel
more frequently to participate in more social activities and opportunities. However, it seems that the
subsidy was not adequately distributed to the target group (low income) with 40.31% of non-low income
who did not really receive the benefit of free train policy. The cause might be that Thai government had
subsidized the budget to everybody for this policy, and subsequently third-class seat was not reserved
for only low-income (intended benefit), but non-low-income could also access free train. Another
reason was that some non-low-income users typically used third class seat before the implementation
of free train policy. When the free train policy was started, these former users were implicitly forced to
use free third-class seat.
For the implication, to remove 40.31% of the unintended benefit, the strategy for separating who is low-
income should be developed. The Specific Identification Card only for low-income for using free train
was recommended. The card should be given to only low-income persons who evidentially prove their
income level to the official. The example of how to define low-income is by classifying based on
taxation. The card registration may be done by many channels such as internet, mailing, phone, or face-
to-face interview. This card has to be shown with their typical identification card at the ticket booth to
get free ticket. As a result, non-low-income has to pay for tickets. This strategy can apparently eliminate
the 100% of the unintended benefit.
69
CHAPTER 7 THE INVESTIGATION OF SOCIAL EXCLUSION
CAUSED BY CURRENT TRANSPORT DIFFICULTY OF BANGKOK
ELDERLY
7.1 Methodology
This part aimed to investigate what aspects of transport performances were unsatisfied by Bangkok
elderly people and caused feeling social exclusion, as well as recommend hot to improve those
unsatisfied performances. The assumption of this section was that the inconvenient transportation of
elderly people, might reduce their degree of satisfaction with transportation and limit their
transportation ability, leading to feelings of social exclusion. However, the feeling of social exclusion
might not be directly caused by unsatisfactory transportation, but it might be caused by the social
participation deficit (mediator), represented by the gap in number of trip. Therefore, to examine if
unsatisfactory transportation had a connection with the feeling of social exclusion, our analysis aimed
to investigate the relationships among the following three elements: 1) unsatisfactory transportation
measured by satisfaction degree of daily transportation, 2) the deficit of trip frequency to participate in
social services and activity (gaps in number of trips), and 3) Degree of feeling of social exclusion of
Bangkok elderly. In addition, socio economic factors were included in the framework analysis because
variation of physical characteristic, personality and living condition of elderly people might affect the
amount of gap in number of trip. The methodology of this section is explained below.
7.1.1 Data collection and the measurement of variables
Bangkok, the capital city of Thailand was selected as the study area are because Bangkok was the city
that had the largest number of population in Thailand, as well as number of elderly population. To
obtain the important information for the statistical analysis, in March 2016, 201 samples were collected
from elderly people in Bangkok aged 60 or over as the location shown in Figure 7-1. The interviews
were conducted at gathering places and major transit connection points in Bangkok. The questionnaire
consisted of the grouped questions described below.
A) Socioeconomic information
Questions were asked about Socioeconomic information to observe varying backgrounds of elderly
which might exhibit different patterns of social exclusion. Respondents were asked questions about
their living condition, including personality [from 1 (considerably introverted: prefer staying at home)
to 7 (considerably extroverted: prefer going out to engage in social activity)], number of family
members, degree of social assistance to support their transportation needs [from 1 (lowest) to 7
(highest)], physical health condition related to transportation ability [walking, vision and driving
abilities ranked from 1 (very poor) to 7 (very good)] and general information such as age, gender and
income.
70
Figure 7-1: The survey location
B) Transportation and degree of satisfaction
To observe the degree of satisfaction with daily transportation, questions were asked about
transportation mode and other transportation-related information, and respondents were required to
assess their degree of satisfaction with their transportation [from 1 (completely dissatisfied) to 7
(completely satisfied)]. Transportation mode was categorized into 6 groups, including non-motorized
(walking and cycling), private vehicle (all types of vehicle that elderly drive by themselves), transit (all
types of buses, including large, medium and small size buses), metro (including BTS and MRT), taxi
(excluding motorcycle taxi because no elderly in this sample set used this mode) and relying on others.
Relying on others means the case that elderly was dependent on others (family member or friend living
in their neighborhood) who could support them for travel needs; for example, one of family member
drove elderly to where elderly want to go.
C) Desired level of social participation
To observe the gaps in number of trips, questions were asked about the unwanted gap in the number of
trips (per week), which refers to the difference between the numbers of desired and actual trips taken to
participate in social activities. To answer the desired trip frequency was a hypothetical situation when
the transportation was supposed to be satisfied by elderly people, but the other living conditions, such
as income and living location remained the same. To get the reliable answer, it was important to use the
interview method to make respondents understand this hypothetical situation before asking this
question.
Trip purposes were categorized into the following two groups: 1) mandatory activities such as shopping,
administrative, financial and health activities (health checkup and going to the hospital); and 2) non-
mandatory activities such as visiting family or friends and pursuing hobbies (leisure, sport, and
71
recreation). Irregular trips that were rarely made, such as annual journeys, were not included in the
analysis. In addition, respondents were asked to rate the degree of importance of each trip [from 1 (not
important) to 7 (very important)].
D) Degree of feeling social exclusion
To measure the degree of social exclusion, questions asked about psychological indicators adopted from
specific psychological questionnaires of elderly's quality of life and well-being (Bowling et al., 2013;
Endicott et al., 1993; Kaneda et al., 2011; Kneale, 2012; Raphael et al., 1995). The score of
indicators were ranked from 1 (strongly disagree) to 7 (strongly agree) that represent the feeling of
social exclusion in five dimensions:
1. You are not part of society;
2. You have inadequate relationships with relatives;
3. You have inadequate relationships with friends;
4. You are unable to participate in social activities; and
5. You are unable to access social resources and opportunities.
To investigate whether the gap number of trips affected feelings of social exclusion, respondents were
required to assess their psychological scores twice, i.e., once for each situation of actual and desired trip
frequencies. For the psychological scores at the situation of desired trip frequency, the respondents had
to rate their psychological scores if they were supposed to be able to travel as many time as they wanted
to (the same hypothetical situation of the question mentioned in part b). Therefore, to get the reliable
answer, it was necessary to explain elderly clearly about hypothetical situation before asking them to
give the interviewer answer. The full questionnaire (both English and Thai version) and survey pictures
are shown in Appendix B-1.
7.1.2 To examine the relationship between satisfactory with transportation, socio demographic
and gaps in number of trips (model 1)
The gap in number of trip between existing and desired trip frequency was count data. Therefore, the
study applied count data regression analysis to examine the relationship between satisfaction degree
with transportation, socio economic factors and the gaps in number of trips. The dependent variable y
is gaps in number of trips as written below.
0 time per week
1 time per week
yi= 2 times per week
…
N times per week
For independent, there are many aspects of level of service of transportation services. One aspect might
coordinate with other aspects, so the factor analysis method was applied to group those similar aspects
to gather before applying to the count data regression analysis. Sociodemographic of respondents was
also added to the analysis of the model as independent variables. The example of count data Poisson
and binomial regression functions used in the analysis are expressed as Equation 7-1 and 7-2,
respectively (Long, 1997).
72
𝐥𝐧(𝒀) = 𝒃𝟎+𝒃𝟏𝑿𝟏 + ⋯ + 𝒃𝒌𝑿𝒌 + 𝜺 (7-1)
𝐥𝐧 (𝒀
𝟏 − 𝒀) = 𝒃𝟎+𝒃𝟏𝑿𝟏 + ⋯ + 𝒃𝒌𝑿𝒌 + 𝜺 (7-2)
Where;
Y : the gaps in number of trips,
X1, X2 ,…, Xk : each independent variable,
β0 : y-intercept or the expected value of ln(Y) when all xi are zero,
β1, β2,…,βn : coefficients for Xi1, Xi2,…, Xik, receptively, and
ε : error value that is expected to be zero.
7.1.3 To investigate the relationship between gaps in number of trips and feeling of social
exclusion (model 2)
The degree of social exclusion could be evaluated by using Likert scale which was ordinal data (low
score refers to person with higher degree of feeling social exclusion). Thus, ordered logit regression
analysis was applied to investigate the relationship between the gap in number of trip and the degree of
feeling social exclusion.
The dependent variable y is degree of feeling social exclusion in five dimensions (1. you are not part of
society; 2. you have inadequate relationships with relatives; 3. you have inadequate relationships with
friends; 4. you are unable to participate in social activities; and 5. you are unable to access social
resources and opportunities, as mentioned above) measured by Linkert scale as below.
1: Strongly disagree
2: Disagree
3: Slightly disagree
yi= 4: Fair
5: Slightly disagree
6: Agree
7: Strongly agree
independent variable is gaps in number of trip of 1) mandatory activities such as shopping,
administrative, financial and health activities (health checkup and going to the hospital); and 2) non-
mandatory activities such as visiting family or friends and pursuing hobbies (leisure, sport, and
recreation). Figure 7-2 illustrate the threshold between each gap in number of trips. The probability
and the utility function of the ordered logit model are expressed as Equation 7-3 and 7-4, respectively
(Greene, 2009).
𝑼𝒊 = ∑ 𝜷𝒌𝑿𝒌 +
𝒌
𝜺 (6-3)
𝑷𝒋|𝒋+𝟏 =𝟏
𝟏 + 𝐞𝐱𝐩(𝑼𝒊 − 𝜽𝒋|𝒋+𝟏) (6-4)
73
Where;
pi : the degree of feeling social exclusion measure by Linkert scale for personi,
Ui : utility function for personi,
Xi1, Xi2 ,…, Xin : each independent variable,
β0 : y-intercept or the expected value of Ui when all Xi are zero,
β1, β2,…,βn : coefficients for Xi1, Xi2,…, Xik, receptively, and
ε : error value that is expected to be zero,
𝜽𝒋 : threshold between each level as shown in Figure 6-2.
Figure 7-2: The conceptual diagram of thresholds in ordered logit model when the number of
categories can be varied by the number of range of dependent variable
7.1.4 To recommend how to increase the satisfaction degree of transportation services (model 3)
To investigate possible methods of improvement the degree of satisfaction with transportation of
elderly, the ordered logit models between dependent variable (y: degree of satisfaction with
transportation) and the explanatory variable (x: transport information) were conducted. The dependent
variable was measured as the ordered data as written below.
1: Strongly dissatisfy
2: Dissatisfy
3: Slightly dissatisfy
yi= 4: Fair
5: Slightly Satisfy
6: Satisfy
7: Strongly Satisfy
The independent variables of this model were the physical transport information and condition of each
transportation mode (non-motorized, private car, transit, metro and paratransit), such as walkway
condition, service frequency, service reliability, on-board-seat condition and service convenience. The
probability and the utility function of the ordered logit model are expressed as Equation 7-5 and 7-6,
respectively (Greene, 2009).
74
𝑼𝒊 = ∑ 𝜷𝒌𝑿𝒌 +
𝒌
𝜺 (7-5)
𝑷𝒋|𝒋+𝟏 =𝟏
𝟏 + 𝐞𝐱𝐩(𝑼𝒊 − 𝜽𝒋|𝒋+𝟏) (7-6)
Where;
pi : the degree of satisfaction with transportation measure by Linkert scale for
personi,
Ui : utility function for personi,
Xi1, Xi2 ,…, Xin : each independent variable,
β0 : y-intercept or the expected value of Ui when all Xi are zero,
β1, β2,…,βn : coefficients for Xi1, Xi2,…, Xik, receptively, and
ε : error value that is expected to be zero,
𝜽𝒋 : threshold between each level as shown in Figure 6-2.
7.1.5 To examine all relationships simultaneously by structural equation model (SEM)
To investigate the indirect effect of unsatisfactory with transportation and sociodemographic on the
feelings of social exclusion Bangkok elderly, SEM was applied. If the analysis consists of several
groups of variables, SEM technique was considered appropriate for application to analyze large number
of endogenous, exogenous and unobserved variables (latent variables) that could simultaneously offer
the confirmation of factor analyses, regression path analyses and correlations among all groups of
variables.
To develop the model, the assumption in this study was that the increases in the degrees of feelings of
social inclusion of elderly might be not directly caused by only unsatisfactory transportation. This
feeling social exclusion (measured by the psychological indicators of feelings social exclusion ranked
by 1: strongly disagree and 2: strongly agree) might be resulted by the deficits in the degrees of social
participation (measured by gaps in number of trips (trip per week)), caused by unsatisfactory
transportation (measured by the degrees of satisfaction with transportation ranked from 1: completely
dissatisfied to 7: completely satisfied) and the age-characteristic and living condition of elderly.
Therefore, the process of the increases in the degrees of social exclusion was divided into two processes.
Based on the assumption of the study, the first process, the degrees of satisfaction with transportation,
socio demographic and living condition of elderly were considered as the explanatory variables (The
first layer of the structure of SEM), which affected the decisions to go out or the gaps in number of trips
of elderly (mediated variables in the second layer of the SEM). The second process, these mediators
affected the degrees feeling social exclusion (endogenous variables in the third layer). Nevertheless, the
direct effect of unsatisfactory of transportation (by itself) on the degree of social exclusion had to be
also investigated by path analysis in SEM.
Because the data contained many information collected by respondents, the factor analysis approach
was applied to cluster the large number of variables into each latent, mediated, and endogenous variable.
After all variables were grouped by factor analysis, a path analysis method was applied to observe the
relationships among the variables. Only significant paths with p value less than 0.1 would be considered
significant in the model. The function of the model modifier took part in how to select the appropriate
75
paths. Thus, the SEM would illustrate only the significant paths. The model with CFI greater than 0.9
and RMSEA less than 0.6 was considered to be acceptable in terms of evaluation for goodness-of-fit of
the SEM.
Finally, for this section, the framework of the analysis from model 1 to 3 including variable and path of
the relationship is illustrated in Figure 7-3, and the structure of SEM is shown in in Figure 7-4.
Figure 7-3: The structure of the analyses
Figure 7-4: The structure of SEM
7.2 The Result Analyzed by Regression Methods
7.2.1 Socio-economic characteristics
Socioeconomic information about the 201 respondents is summarized in Table 7-1. The proportion of
men (59.70 percent) was slightly larger than the proportion of women. Half of the respondents were
from the young-elderly group (60 to 64 years old), and approximately one-fifth of the respondents were
single or widowed. In terms of family size, Bangkok seniors tended to live with a higher number of
76
family members (3.56 persons) than their counterparts in the US, where 80% or more of the elderly
lived either alone or with only one other person (Jansuwan, Christensen, and Chen 2013; Hwang et al.
2015). In terms of employment, only a small proportion of the sampled elderly were still working and
following fixed schedules. Therefore, most of the respondents tended to have more free time each day
than members of younger groups. Nevertheless, the income of 46.77 percent of elderly was less than
34,500 JPY per month, which is low compared to the average monthly wage in Thailand (13,495.58
Baht per month) (Trading Economics, 2016). The respondents’ average walking ability (4.58 points)
and vision (4.47 points) were moderate. Although about half of them were able to drive a car (51.74
percent) and ride a motorcycle (54.23 percent), the average driving ability was relatively low (3.68
points and 3.15 points for car and motorcycle, respectively).
Table 7-1: Socio economic information.
General information
N %
Gender
Male 120 59.70
Female 81 40.30
Status
Single or widowed 35 17.41
Married 166 82.59
Employment status
Non-worker 115 57.21
Fixed schedule working 35 17.41
Non-fixed schedule working 51 25.38
Vehicle ownership (multiple choice)
Car ownership with driving driver’s license 104 51.74
Motorcycle ownership with driving driver’s license 109 54.23
Age (year)
60–64 101 50.25
65–69 58 28.86
70–74 28 13.93
>=75 14 6.97
Income (JPY per month)
<=34500 94 46.77
34501–69000 26 12.94
69001–103500 34 16.92
103501–138000 24 11.94
>138000 23 11.44
Avg. Std.
Health status (ranked from 1=very poor to 7=very good)
Walking 4.58 1.24
Vision 4.47 1.22
Ability to drive a car 2.68 1.87
Ability to drive a motorcycle 2.15 1.61
Personality and living condition
Free time (hours per day) 8.53 3.73
Number of members in family (person) 3.56 1.09
Personality (ranked from 1=introverted to 7=extroverted) 4.14 1.75
Degree of support from transport assistance for transportation (ranked
from 1=strongly disagree to 7=strongly agree)
3.06 1.22
77
7.2.2 Trip purposes
Trip purposes and frequencies are presented in Figure 7-5, in which one trip refers to one occurrence
of an activity. The highest trip frequency involved shopping (2.64 trips per week), followed by hobbies
(1.66 trips per week), meeting friends (1.58 trips per week) and visiting relatives (1.36 trips per week).
Although mandatory activities to meet basic human needs tended to be considered more essential among
people in general age groups (such as people of working age), shopping and non-mandatory activities
were more important to Bangkok’s elderly. The reason may be that mandatory activities seemed like
duties for the elderly, who preferred activities that could satisfy their desires and make them happy late
in life. In this case, the shopping habits of Thai elderly people might be relatively dissimilar from those
of the elderly in developed countries. Based on the interviews, the respondents seemed to enjoy daily
shopping because they could also walk, relax, and chat with others at the same time. Accordingly,
shopping was also considered a leisure activity.
The results showed that the gaps between the number of desired and actual shopping and non-mandatory
activities were larger than the gaps associated with mandatory activities. The respondents did not
engage in trips involving shopping and non-mandatory activities at their frequency. The gap between
the desired and existing numbers of shopping and non-mandatory activities should be reduced to fulfil
the travel needs of elderly people in Bangkok. Nevertheless, respondents did not report other preferred
destinations because they tended to already be familiar with existing places and with the people there.
It could be that the respondents’ destination choices were also strongly affected by the social
connections available there.
Figure 7-5: Degree of importance of activities and gap in desired number of trips by activity.
Shoppin
g
Admini
strative
Financi
al
activity
Health
activity
Visiting
family
Meeting
friendHobby
Degree of activity’s importance 3.89 2.66 2.85 4.18 6.10 6.02 5.10
Existing number of trips 2.64 0.54 0.80 0.88 1.36 1.58 1.66
Desired number of trips 3.58 0.55 0.91 0.91 2.31 2.42 2.15
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
Aver
age
nu
mb
er o
f tr
ip p
er w
eek
Deg
ree
of
acti
vit
y’s
im
po
rtan
ce
78
7.2.3 Daily transportation and gaps in number of trips
A) Transportation mode
The transportation modes used to reach activity destinations are categorized by trip purpose and
presented in Figure 7-6. The majority of the transport modes used were transit (24.14%), followed by
private vehicle (18.41%), metro (9.08), taxi (6.93%) and non-motorized transportation (6.51%). The
proportion of Bangkok’s elderly that relies on others for transportation was 32.36% was larger than that
were found in previous studies in US (25.00%) and Canada (10.00%) (Hwang et al., 2015; Jansuwan
et al., 2013). However, the average degree of transport assistance for transportation needs, which refers
to the frequency that elderly’s family member or neighbourhood supported them by driving them to
where they wanted to go, was only 3.06 of 7, according to Table 7-1. The statistics indicated that the
proportion of the elderly who had social supporters was significantly larger during weekends (51.37%)
than during weekdays (14.85%). It seemed that their assistants worked on workdays (Monday to
Friday), and the temporal mismatch between the elderly and social assistants was not likely to be
remedied. Therefore, on workdays, most of the elderly needed to use their own transportation. Except
for the respondents who travelled by non-motorized mode, non-drivers had to rely on public
transportation (43.41%) and taxi (8.91%). However, approximately half of the weekday trips made by
public transportation and taxi (52.33%) were for shopping and non-mandatory activities. However, the
actual trip frequencies of these activities were not satisfied according to the data in Figure 7-6.
Figure 7-6: Mode uses and gaps of the numbers of trip by mode
0
50
100
150
200
250
300
Wee
kd
ay
Wee
ken
d
Gap
Wee
kd
ay
Wee
ken
d
Gap
Wee
kd
ay
Wee
ken
d
Gap
Wee
kd
ay
Wee
ken
d
Gap
Wee
kd
ay
Wee
ken
d
Gap
Wee
kd
ay
Wee
ken
d
Gap
Non-
motorized
Private
vehicle
Transit Metro Taxi Relying on
others
Num
ber
of
trip
in o
ne
wee
k
Shopping Administrative Financial activity Health activity
Visiting family Meeting friend Hobby
79
B) Gap in number of trips categorized by transportation mode
According to Figure 7-6, elderly taxi users had the highest total gap in the number of trips, as a
percentage of existing trip rate (total gap in number of trips divided by total actual trips made by taxi in
both weekday and weekend, 74.04%), and those who travelled by non-motorized mode had the smallest
total gap (18.39%). The gaps in number of shopping and non-mandatory trips were significantly larger
than those of mandatory trip for every transportation modes. It is possible that the gap between the
numbers of desired and actual trips was caused by dissatisfaction with aspects of daily transportation
and socioeconomic characteristics of elderly people. Statistical analyses were conducted to investigate
the effect of the degree of satisfaction with transportation on the gap in the number of trips. First, factor
analysis was conducted to avoid the problem of multicollinearity, as shown in Table 7-2. Subsequently,
count data regression analysis between explanatory variables (x: degree of satisfaction with
transportation and person’s characteristic) and the dependent variable (y: gap in the number of trips)
was performed, as presented in Table 7-3. The count data model with the lowest Akaike Information
Criterion (AIC) was selected as having the best fit. In this case, the regression analysis for those who
needed to rely on others for transportation is not provided.
First, according to Table 7-2, the variable about degrees of satisfaction with transportation (S_WALK,
WS_PRI1, WS_TRANSIT1, WS_METRO1, WS_TAXI1 and WS_TAXI2: see the detail of each
variable in Table 3) had negative significant relationships to the gaps in number of trip. It can be implied
that elderly people with lower degrees of satisfaction with transportation tended to have larger gaps
between the desired and actual numbers of activities. It seems that although the elderly would like to
travel to participate in society, unsatisfactory transportation discouraged them from going out, resulting
in the sacrifice of a portion of their desired trips.
Second, both age-related characteristics and living conditions affected respondents’ decision to go out.
The variable H_W and P1 had negative significant relationship to the outcome of non-motorized mode.
It seems that elderly people who travelled by non-motorized modes with higher walking ability and
degrees of transport assistance tended to have smaller gaps in the number of trips. In this case, the
caretakers who looked after the elderly when they walked or rode bikes offered important support for
their travel, especially for those with lower walking ability. Number of family member (N_F) also had
significant negative relationships to the outcomes of private vehicle, transit and metro. It can be implied
that elderly living in larger family size had less gaps in number of trip made by these modes because
they might be already satisfied with participating in many kinds of home activities with their family
members, and subsequently had less motivation to go out. In addition, their family member might be
able to support them for travel needs that could made the gaps in number of trip smaller.
In terms of age (AGE), the variable AGE had positive and negative relationships to the outcomes of
private car and transit, respectively. I may be that elderly private car users with higher age tended to
have larger gaps in number of trips that was opposite to those of elderly transit users. Therefore, the
results showed the contrast to the typical image of the elderly that elderly people with higher age did
not always want to go out more frequently, as was generally seen. For the aspect of income, the variable
INC had negative relationship with the outcome of transit. It seems that elderly transit users with lower
income tended to have larger gaps in number of trip.
80
Table 7-2: Factor analysis of degree of satisfaction with transportation
Transportation Mode
►
No
n-m
oto
rize
d
Pri
va
te v
ehic
les
Tra
nsi
t
Met
ro
Ta
xi
Aspect ▼ -
Fa
cto
r1
Fa
cto
r2
Fa
cto
r1
Fa
cto
r2
Fa
cto
r1
Fa
cto
r2
Fa
cto
r1
Fa
cto
r2
Fa
cto
r3
Access walkway
distance, condition and
environment
U
(4.14) - -
0.847
(3.90)
0.850
(3.98)
0.833
(4.41)
Safety aspect U
(4.20)
0.846
(5.31)
0.854
(4.15)
0.843
(4.57)
0.858
(3.86)
Comfort and
convenience
U
(5.20)
0.884
(3.57)
0.860
(3.91)
0.909
(4.45)
0.935
(5.20)
Fare or travel cost - U
(5.04)
U
(5.04)
0.794
(5.66) 0.345
(3.11)
0.939
(2.83)
Road traffic condition - 0.874
(3.90)
0.800
(4.02) - -
0.878
(4.41)
Parking space - 0.855
(3.89) - - - - - - -
Space in vehicle - - - 0.845
(3.69)
0.777
(3.86) - - -
Seat availability - - - 0.851
(3.01)
0.876
(3.23) - - -
Service frequency - - - 0.685
(4.95)
0.584
(5.64) - - -
Service information
system - - -
0.815
(3.94)
0.686
(5.07)
U
(4.65)
U
(4.65)
U
(4.65)
Reliability of punctuality - - - 0.676
(4.56)
0.528
(5.55)
0.810
(4.28)
Reliability of driver - - - - - - - 0.886
(3.85)
Kaiser-Meyer-Olkin
(KMO) - 0.648 0.88 0.85 0.633
Name of grouped
variable -
WS
_P
RI1
WS
_P
RI2
WS
_T
RA
1
WS
_T
RA
2
WS
_M
ET
1
WS
_M
ET
2
WS
_T
AX
I1
WS
_T
AX
I2
WS
_T
AX
I3
Note: The number in the parenthesis () is the mean of degree of satisfaction with each aspect (1: completely dissatisfied
to 7: completely satisfies); - is non-applicable; and u is unloaded factor but still used to fit the model
For personality aspect, the variable P2 had negative relationships to the outcomes of non-motorized
mode, private vehicle and metro. It may be that some of the older people were introverted and had less
motivation to go out by these modes than did people who were more extroverted.
In addition, the existing trip rate (EN) affected the size of the gap between the numbers of desired and
actual trips made by Taxi according to the negative significant relationship. Elderly taxi users who could
travel more often in their current situation tended to have smaller gaps because they were already
satisfied with their existing trip rate.
81
Table 7-3: Count data regression analyses of gaps in the numbers of trips categorized by mode
Variable
Non-
motorized
mode
Private
vehicle Transit Metro Taxi
Coef. Coef. Coef. Coef. Coef.
S_WALK -0.108**
WS_PRI1
-0.098*
WS_TRANSIT1
-0.515***
WS_METRO1
-0.101**
WS_TAXI1
-0.247***
WS_TAXI2
-0.114*
P1 -0.122**
P2 -0.203*** -0.112***
-0.234***
H_W -0.140*
NUM_F
-0.164*** -0.201* -0.136**
EN
-0.080*
AGE
0.051** -0.109***
INC
-0.001***
Constant 3.637 -0.307 10.357 2.984 2.954
Number of obs. 51 70 109 58 46
LR chi2 0.141 0.135 0.124 0.123 0.110
Log likelihood -110.583 -144.237 -176.760 -127.004 -89.458
BIC 236.893 305.468 372.285 298.312 190.402
Note: * is 90% significance level, ** is 95% significance level and *** is 99% significance level
When:
S_WALK = Satisfaction level with walkway condition and environment
WS_PRI1 = Weight score 1 of private vehicle (from table 3)
WS_TRANSIT1 = Weight score 1 of transit (from table 3)
WS_METRO1 = Weight score 1 of metro (from table 3)
WS_TAXI1 = Weight score 1 of taxi (from table 3)
WS_TAXI2 = Weight score 2 of taxi (from table 3)
EN = Existing number of all activities (time per week)
AGE = Age (year)
INC = Income (Baht per month)
NUM_F = Number of member in family (persons)
H_W = Walking ability (1: very poor to 7: very good)
P1 = Degree of transport assistance to support transportation needs
(1: strongly disagree to 7: strongly agree)
P2 = Personality (1: considerably introverted to 7: considerable
extroverted)
S_WALK = Satisfaction level of walkway condition and environment (1-7)
7.2.4 Measurement of the degree of social exclusion
A) Psychological scores
The difference in the respondents’ psychological scores with respect to existing and desired trip rates
is shown in Figure 7-7. In the existing situation, the psychological scores of degrees of social exclusion
of the elderly in most dimensions were approximately 3 and over, except for the dimension of being
82
part of society, for which the average score was only 5.50 of 7. It seemed that although Bangkok’s
senior citizens thought that they were moderately able to participate in social activities and
opportunities, they had a serious problem in that that they felt socially isolated. However, the score of
this dimension most significantly reduced by 2.41 if they were able to travel to participate in society at
their desired frequency.
B) Effect of gaps in the numbers of trips on psychological scores
The statistics indicated that psychological scores in all dimensions increased if the elderly had the ability
to travel at their desired frequency. Although they travelled to participate in several social activities, the
issue of which activities most affected the psychological score should be investigated. Therefore,
ordered logit models between the explanatory variable (x: gap in the numbers of trips for each activity)
and the dependent variable (y: psychological scores) were conducted, as shown in Table 7-4.
Figure 7-7: Differences of the feelings of social exclusion between the situation of existing and
desired trip frequencies
According to Table 7-4, the statistical models showed that only the gap in the numbers of shopping
(GAP_A1) and non-mandatory trips (GAP_A2 to A4) affected the feelings of social exclusion in five
dimensions. This result corresponded to the degree of trip importance shown in Figure 2. Therefore, to
achieve the goal of social inclusion, it is important to design approaches to supporting elderly people’s
ability to travel to places for shopping and non-mandatory activities, especially to visit relatives, meet
friends and participate in hobbies, which were significantly related with the most unsatisfied dimension,
being part of society. One possible approach was to increase the degree of satisfaction with
transportation to reduce the gap in the numbers of trips, as discussed in the next section.
C) The simulation of psychological scores categorized by transportation modes
As shown in Figure 7-6, Bangkok’s elderly had different number of gaps in number of shopping and
non-mandatory trips according to different transportation modes. Therefore, the study applied the
ordered logit models in Table 7-4 to simulate the psychological scores from different amounts of gaps
5.50
3.84 3.823.16 3.233.09
2.80 2.632.06
2.50
1
2
3
4
5
6
7
Not Being part
of society
Having
inadequate
relationships
with relatives
Having
inadequate
relationships
with friends
Being unable to
participate
social activities
Being unable to
access social
resource
Psy
cholo
gic
al s
core
Situation of existing trip frequency Situation of desired trip frequency
83
in number of shopping and non-mandatory trips for each transportation mode as shown in Figure 7-8.
The simulation shows that elderly travelling by non-motorized mode had the lowest degree of feelings
social exclusion inn all dimensions but taxi users with the largest gaps in number of trips (according to
Figure 7-6) had the highest degree in every dimension, especially the dimension of not being part of
society. In addition, the feelings of social exclusion of transit and metro users were relatively higher
than those of non-motorized and private car users. It can be summarized that those who travelled by
non-motorized mode and private vehicle had lower degrees of social exclusion. On the other hand, taxi,
transit and metro users had higher degrees of social exclusion in various dimensions.
As mentioned above, a large proportion of elderly people needed to rely on others to travel to their
activity destinations, but they tended to have assistance only on weekends because of temporal
mismatch. Therefore, most elderly people (69.75%) needed to rely on their own transportation on
weekdays. The statistics indicated that most such trips were made by public transport and taxi (52.33%),
as shown in Figure 7-6. Thus, improving the degree of satisfaction with public transport and taxi would
significantly contribute to promoting social inclusion among Bangkok senior citizens.
7.2.5 The approach to improve the degree of satisfaction with transportation
To investigate possible methods of improvement, the ordered logit models between the explanatory
variable (x: transport information) and the dependent variable (y: degree of satisfaction with
transportation aspects significantly related to the outcome in Table 7-3) were conducted, as shown in
Table 7-5. It must be noted that there was no significant variable for non-motorized travel mode and
private vehicle.
Table 7-4: Ordered logit models of the psychological scores
Variable
Bei
ng p
art
of
the
soci
ety
Hav
ing a
deq
uat
e
rela
tionsh
ips
wit
h
rela
tives
Hav
ing a
deq
uat
e
rela
tionsh
ips
wit
h
frie
nds
Bei
ng a
ble
to p
arti
cipat
e
soci
al a
ctiv
itie
s
Bei
ng a
ble
to a
cces
s
soci
al r
esourc
e
Coef. Coef. Coef. Coef. Coef.
GAP_A1 0.480*** -0.277** 0.838***
GAP_A2 0.798*** 0.812***
GAP_A3 0.346*** 0.702*** 0.568***
GAP_A4 0.800***
0.946*** 0.347***
cut1 -3.538 -3.617 -3.835 -4.851 -5.780
cut2 -2.362 -2.556 -2.699 -4.114 -3.542
cut3 -0.823 -1.619 -1.791 -3.041 -2.429
cut4 0.056 -0.597 -0.627 -1.429 -1.188
cut5 0.997 0.363 0.260 -0.532 -0.074
cut6 2.387 1.221 0.898 0.555 0.904
Number of obs. 201 201 201 201 201
Pseudo R2 0.294 0.341 0.249 0.200 0.209
Log likelihood Max -201.37 -174.67 -211.21 -216.53 -176.22
BIC 423.953 354.643 433.027 443.667 363.047
Note: * is 90% significance level, ** is 95% significance level and *** is 99% significance level
84
When:
GAP_A1 = Gap in number of shopping trip (trip per week)
GAP_A2 = Gap in number of visiting relative trip (trip per week)
GAP_A3 = Gap in number of meeting friend trip (trip per week)
GAP_A4 = Gap in number of hobby trip (trip per week)
According to the Table 7-5, the average service frequency of transit (S_FRE of WS_TRANSIT1) was
4.7 times per hour, but the probability of the elderly finding a seat (P_SEAT of WS_TRANSIT1) on
the vehicle was only 48.1%, which was a serious problem. As implied by the statistical model in Table
6, bus service frequency should be increased during weekdays, when most of the elderly had to rely on
their own transportation, especially on the routes that link them to places to shop and engage in non-
mandatory activities. As the number of buses per hour increases, the probability that the elderly will
find a seat will also increase. In addition, the number of priority seats can be increased without a
substantial impact on service performance because when no elderly passenger is present, other
passengers can sit on those seats.
The cost of the metro (T_COST of WS_METRO1) tended to affect the degree of satisfaction with the
form of transportation. The average fare (5.4 Baht per kilometre) was more expensive than that of other
modes of public transportation such as transit (only 2.1 Baht per kilometre from the data). However,
metro and bus fares have been discounted by 50% for all senior citizens. Like transit, the probability of
finding an available seat on the metro (P_SEAT of WS_METRO1) was only 41.7%. This problem can
also be solved by adding priority seats. In terms of walking distance to access points, the number of
metro stations was much lower than that of transit stations. Thus, metro stations covered smaller areas
than bus stops, causing a longer average walking distance (AC_WALK of WS_METRO1) to metro
stations (579.0 meter) than the distance to transit stations (303.0 meter). For this range of access
distance, it is possible to introduce a feeder system, such as a small bus or van, to carry elderly people
to metro stations, especially on weekdays.
Figure 7-8: The simulation of psychological scores in five dimensions categorized by modes
01234567
Not Being part of
society
Having inadequate
relationships with
relatives
Having inadequate
relationships with
friends
Being unable to
participate social
activities
Being unable to
access social
resource
Non-motorized
Private vehicle
Transit
Metro
Taxi
Relying on others
85
For elderly taxi users, walking distance was not a problem because taxis offer door-to-door service.
However, the proportional fare (excluding a 35-Baht fixed charge: TAXI_FARE of WS_TAXI2) was
expensive for them and thus affected their degree of satisfaction. Furthermore, some taxi drivers
(20.4%) unreasonably denied elderly passengers (P_DENY of WS_TAXI1). The mechanism was that
driver denied picking up elderly passenger when elderly called the taxi. Therefore, they needed to call
many taxis until they found a driver who was willing to service them. In this case, making the fee per
ride of taxi cheaper and improve the reliability of drivers are recommended in order to improve the
degree of satisfaction of the use of taxi.
Table 7-5: Ordered logit models of degrees of satisfaction with transportation
Variable Transit Metro Taxi
WS_TRANSIT1 WS_METRO1 WS_TAXI1 WS_TAXI2
Coef. Coef. Coef. Coef.
AC_WALK -0.010***
(303.0)
-0.007**
(579.0)
S_FRE 0.446***
(4.7)
-
(14.24)
P_SEAT 0.062***
(48.1)
0.070***
(41.7)
T_COST -
(2.1)
-0.208*
(5.4)
TAXI_FARE
-0.146**
(7.6)
P_DENY
-0.202***
(20.4)
INC 0.001*
(64523)
0.001***
(78640)
H_W 0.091*
(3.21)
0.045**
(4.21)
cut1 -3.784 -5.307 -14.139 -4.498
cut2 -0.415 -4.444 -10.687 -2.470
cut3 3.321 -0.981 -8.203 -1.210
cut4 4.411 -0.087 -4.552 -0.254
cut5 6.809 2.777 0.104 1.542
cut6 11.995 5.104 1.699 2.834
Number of obs. 109 58 46 46
Pseudo R2 0.424 0.243 0.445 0.154
Log likelihood Max -90.455 -62.004 -40.365 -60.422
BIC 201.411 142.08 84.559 138.041
Note: * is 90% significant level, ** is 95% significant level, *** is 99% significant level and the
number in the blanket is mean of each explanatory variable
When:
AC_WALK = Access walking distance (meters)
S_FRE = Service frequency (times per hour)
P_SEAT = Probability that respondent gets a seat on vehicle (%)
T_COST = Travel cost (Baht per kilometre)
TAXI_FARE = Taxi proportional fare (excluding 35 Baht of fixed charge: Baht)
P_DINY = Probability that taxi driver denies passenger (%)
INC = Income (Baht per month)
H_W = Walking ability (1: very poor to 7: very good)
86
In current situation, other kinds of paratransit have not been implemented yet. Therefore, elderly had to
rely on only taxi for this kind of mode. In this case, instead of relying on only taxi service, the local
government should provide elderly more complimentary alternative of age-friendly door-to-door
service (targeting elderly passengers), such as carpool, ridesharing and demand responsive transit
(DRT) services. The cost per ride of these kinds of door-to-door services could be brought down until
where the prices are cheaper than those of taxi service, and the drivers could be specially trained to be
ready to support and service elderly people with more reliability.
In addition, some of sociodemographic variables of elderly also had significant relationship to the
degree of satisfaction of transportation. Ability to travel themselves (walking ability: H_W) had positive
relationship to the satisfaction with bus and metro. It seems that the difficulty of walking to access the
station affected the degree of satisfaction with the use of these modes. Income (INC) of elderly also
affected the degree of satisfaction with the use of taxi. Elderly with lower income tended to have less
satisfaction with taxi because taxi was relatively more expensive than other transportation modes.
7.3 The Result Analyzed by SEM Approach
To investigate the process leading to the increases in feelings of social exclusion, because of there were
many groups of variables, the study applied another method which was SEM to analyse all relationships
among all variables simultaneously. However, it has to be note that the analysis applied only the sample
of elderly private car (70 samples), transit (107 samples) and metro (60 samples) users due to the limited
of sample size of non-motorized and paratransit modes for the analysis of SEM.
7.3.1 Socio-economic characteristics of the samples used for the analysis of SEM
First, the descriptive statistic of the sample used for analysing SEM is shown in Table 7-6. The majority
of respondents were from the lower spectrum of the elderly group (60 to 64 years old), while
approximately one-fifth of the respondents were single or widowed. Bangkok seniors tended to live in
medium-size families (average 3.57 members per household). In terms of employment, only
approximately one-sixth of the sampled elderly (14.92 percent) were still working and following fixed
schedules. Therefore, most of the respondents had much free time each day (average 7.57 hours per
day).
The proportion of male drivers (61.43 percent) was slightly larger than that of female drivers (38.57%).
Even though almost half of public users had driving licenses and private cars in their household, they
decided to use public transport for their daily transport. As reported by respondents, driving required
relatively good health in terms of walking, vision and hearing. The average health statuses of private
car users were higher than those of ground public transport users. In addition, the average incomes of
private car and metro users were higher than those of transit users because the travel expenses for fuel
and metro fare were more expensive than the fare for transit.
87
Table 7-6: Socio-economic information of the data used for analysing SEM
Mode uses ► Private car users Transit users Metro users
Frequency data ▼ N % N % N %
Gender
Male 43 61.43 64 59.81 33 55.00
Female 27 38.57 43 40.19 27 45.00
Status
Single or widowed 12 17.14 15 14.02 12 20.00
Married 58 82.86 92 85.98 48 80.00
Age (year)
60-64 51 72.86 75 70.09 43 71.67
65-69 16 22.86 21 19.63 13 21.67
70-74 3 4.29 9 8.41 4 6.67
>=75 0 0.00 2 1.87 0 0.00
Employment status
Fixed schedule working 20 28.57 11 10.28 16 26.67
Non-fixed schedule working 26 37.14 19 17.76 28 46.67
Non-worker 24 34.29 77 71.96 16 26.67
Vehicle ownership
Household car ownership with driving
license
70 100.00 37 34.58 38 63.33
Income (JPY per month)
<=34500 5 7.14 72 67.29 3 5.00
34501–69000 15 21.43 6 5.61 15 25.00
69001–103500 21 30.00 11 10.28 18 30.00
103501–138000 18 25.71 7 6.54 11 18.33
>138000 11 15.71 11 10.28 13 21.67
Continuous data ▼ Average Std. Average Std. Average Std.
Health status (ranked from 1: very poor to
7: very good)
Walking 5.23 0.93 3.89 1.24 4.40 1.21
Vision 5.07 0.95 3.94 1.17 4.07 1.21
Hearing 5.03 0.84 4.10 1.10 3.97 1.06
Personality and living conditions
Free time (hour per day) 6.86 2.96 8.44 2.98 6.83 3.09
Number of member in family (person) 3.70 1.29 3.51 1.05 3.52 1.19
Personality (1: considerably introverted
to 7: considerably extroverted)
3.90 1.92 4.76 1.24 4.68 1.30
7.3.2 Degrees of satisfaction with transportation of the SEM samples
Respondents were asked to assess their degrees of satisfaction with daily transportation in various
aspects, such as walkway conditions, service information and onboard convenience as shown in Figure
7-9. According to Figure 7-9, elderly people felt more comfortable driving to travel than public
transport users. It seemed that car users tended to travel longer distances (average 8.47 kilometres) than
those of transit and metro users (average 4.66 and 5.36 kilometres for transit and metro, respectively).
The fuel cost for private cars was relatively satisfying at an average of 7.05 Baht per kilometre.
According to the interviews, elderly drivers could avoid traffic jams because they did not have to go
out during peak hours, allowing them to avoid the severe traffic congestion. Therefore, the score of
traffic conditions was not as considerably low as in general cases. In addition, the elderly felt safe
driving in urban areas because the average driving speed was relatively slow (11.90 kilometres per
hour). However, the result indicated that it was relatively difficult for drivers to find parking spaces at
the activity destinations.
88
In terms of transit users, the price of transit fare (average 2.30 Baht per kilometre) was considerably
satisfying after it was subsidized for the elderly by the government. Although a portion of metro fares
was already subsidized, the fare was not actually cheap for respondents (6.08 Baht per kilometre).
However, metro transport had much more service frequency (average 14.26 times per hour) than that
of transit (average 4.74 times per hour). Therefore, the average degrees of satisfaction with service
frequency and punctuality of metro users were higher than those for transit service.
As reported by respondents, elderly ability to gain information for metro service was higher than that
of transit users because most important information for metro services was provided at the BTS and
MRT station, as well as on the website. However, there was little information provided at the actual bus
and van stops, making it difficult to understand the overall service with only bus numbers.
The focus point for elderly people tended to be aspects of comfort. The result indicated that the comfort
level in metros was more satisfying than in buses and vans. However, elderly metro users needed to
walk for longer distances in order to access the stations (average 578.96 meters) than those of senior
transit passengers (303.03). Nevertheless, access walkway conditions, such as weather, cleanliness and
widths of walkways, for both transit and metro were not satisfying according to elderly public transport
users in fair health, as seen in Table 7-6 (Transit). In addition, it seemed that the number of priority
seating was not sufficient to serve the elderly because the probability of finding an available seat for
the elderly on buses and metro was only 48.07% and 41.72%, respectively.
Figure 7-9. Degree of satisfaction with daily transportation of the SEM samples
3.89
3.90
5.31
5.57
5.04
4.95
4.57
3.96
4.17
3.91
3.92
3.78
3.69
5.71
5.62
5.50
5.00
4.52
3.97
4.42
3.93
3.85
3.97
0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00
Praking space availability
Traffic condition
Safety on the road
Comfort driving
Fuel cost
Service frequency
Reliability of punctuality
Service information
Service safety
Access walkway condition
Comfort in vehicle
Seat availability
Space in vehicle
Fare cost
Service frequency
Reliability of punctuality
Service information
Service safety
Access walkway condition
Comfort in vehicle
Seat availability
Space in vehicle
Fare cost
Pri
vat
e ca
rT
ransi
tM
etro
Degree of satisfaction
89
7.3.3 Desired levels of social participation of the SEM samples
Subsequently, elderlies were also asked to explain their existing and desired trip frequency to engage
each social activity in order to calculate the gaps in number of trips in 6 kinds of activities, including
shopping, administrative tasks, going to hospital, visiting friends and relatives (VFR) and hobbies, were
set as mediators. Answering the desired trips frequency was a hypothetical situation when using of
public transportation and driving was supposed to be satisfied by elderly, but the other living
circumstances, such as income level and number of family member were the same. To get the reliable
answer, it was important that interviewer had to make respondents understand this hypothetical situation
before letting them answer.
The average numbers of trips made by the elderly in Bangkok were categorized by modes, as shown in
Figure 7-10. According to Figure 7-10, car users made the most number of actual trips among all
transportation users. However, the results indicated that both private car and public transport users had
gaps between existing and desired number of trips. It seemed that both driver and public transport users
were not satisfied with their degree of social participation due to gaps in the number of trips.
Figure 7-10: Gaps in existing and desired number of trips by mode of the SEM samples
The results indicated that metro users tended to make fewer shopping trips because road network and
transit systems could access more shopping places. As well, it seemed that metro users made fewer trips
to go to hospitals. However, it turned out that metro users had the smallest gap in number of trips to
engage shopping and hospital.
Travelling by private car tended to have the highest potential to access administrative activities and
destinations, such as government or district offices, due to having the most frequency of trips made by
car users. However, based on interviews, it seemed that both private car and public transport users did
not want to go to the hospital and do administrative activities more frequently according to very small
gaps in the number of trips for administrative tasks and going to hospital.
1.37
0.41
1.56
0.58 0.24 0.19
1.51
0.11
0.11
0.010.10 0.00
0.46
0.01
0.95
0.000.12
0.00
1.01
0.63
0.92
0.470.88
0.47
1.00
0.60
0.49
0…
0.84
0.45
0.86
0.13
0.82
0.43
1.17
0.48
0
1
2
3
4
5
6
7
Existing
number of trips
Gap in number
of trips
Existing
number of trips
Gap in number
of trips
Existing
number of trips
Gap in number
of trips
Private car users Transit users Metro users
Tri
p p
er p
erso
n p
er w
eek
Shopping Administrative Hospital Visiting friends Visiting relative Hobby
90
Based on interviews, VRF was likely to be the activities based on appointment setting between the
respondents and their friends or relatives. Nevertheless, it seems that the elderly in Bangkok want to
travel more frequently to meet their friends and relatives due to the large gaps in number of VRF trips.
However, it was difficult to explain the causes of the different number of gaps in VFR trips for the
elderly using different transportation modes because the meeting times and locations depended on the
various conditions of their appointments.
In summary, elderly people tended to have a gap in non-duty activities, which were shopping, VFR and
hobbies. From the interviews, the reason may be that duty activities seemed like those for the elderly,
who preferred non-duty activities that could satisfy their desires and make them happier late in life.
7.3.4 Degree of social exclusion of the SEM samples
The average scores for psychological indicators of social exclusion in 5 dimensions (SE1-SE5) are
shown in Figure 7-11. For each dimension, respondents reporting higher scores from 1 to 7 were
considered to have higher degree of social exclusion. According to Figure 7-11, the results indicate that
elderly public transport users tended to feel more social exclusion than those of private car users in the
dimensions of SE1 and SE2. The reasoning may be that car users had a much smaller gap in the number
of hobby trips than those of public transport users according to Figure 7-10. Besides, it seemed that
metro users felt the least socially excluded in the dimension of SE3. The degree of social exclusion in
this dimension might be related to the gap in number of shopping trips reported by metro users, as in
Figure 7-10.
With regard to SE4 and SE5, even though car users had much larger gaps in the number of VFR trips
than those of public transport users, a large difference was not noticed in the degree of social exclusion
in terms of SE4 and SE between car and public transport users. Nevertheless, the scores for SE4 and
SE5 might be related to the gap in number of VRF that would be analysed in the section for statistical
analysis. In summary, it can be seen that the most significant dimensions of social exclusion were SE1
for elderly transit users (5.53), followed by SE1 and SE2 for metro users (4.98 and 4.68, respectively).
Besides, the most significant dimension for senior drivers was SE1 with a score of 4.67. To investigate
the causes of social exclusion for both elderly private and public transport users, SEM was performed
in the next section.
Figure 7-11: Degrees of social exclusion in five dimensions of the SEM samples
4.07
4.10
3.94
3.99
4.67
3.98
3.96
4.33
4.56
5.35
4.08
3.82
3.53
4.68
4.98
0 1 2 3 4 5 6 7
You have inadequate relationships with relatives:
SE5
You have inadequate relationships with friends: SE4
You are not able to access social resources: SE3
You are not able to participate in social activities:
SE2
You are not part of society: SE1
Degree of social exclusion
Metro users Transit users Private car users
91
7.3.5 Structural equation modelling for the process of social exclusion
Analysed from 237 data samples, the standardized SEM for the process of social exclusion in elderly
caused by driving and public transport difficulties was developed as shown in Figure 7-12 and Figure
7-13, respectively. The explanation of variables is written below.
Exogenous variables:
Degree of satisfaction with transport (1: completely dissatisfied to 7: completely satisfied)
D_Com = Comfort of driving
R_Safety = Road safety
Traffic_C = Traffic conditions
Parking_S = Parking space availability
Space = Space in vehicle
Seat_A = Seat availability
S_Com = Comfort in both station and vehicle
Walkw_C = Access walkway condition and environment
S_Safety = Service operation safety and security
S_Info = Ability to obtain and understand service information
S_Relia = Service reliability and punctuality
S_Fre = Service frequency
Health conditions (1: very poor to 7: very good)
Walking = Ability to walk
Vision = Ability to see clearly
Hearing = Ability to hear
Unobserved variables:
SF_PRI1 = Satisfaction with convenience of driving
SF_PRI2 = Satisfaction with traffic conditions
SF_PUB1 = Satisfaction with service convenience
SF_PUB2 = Satisfaction with service operation
Health_C = Health conditions
Mediated variables:
Gaps in number of trips (trips per week)
G_Shopping = Gap in number of shopping trips (trips per week)
G_Hobby = Gap in number of hobby trips (trips per week)
G_VFriend = Gap in number of visiting friend trips (trips per week)
G_VRelative = Gap in number of visiting relative trips (trips per week)
Mediator (latent variable)
SPD_SPO = Degree of social participation deficits in the aspect of social
participation and opportunities
SPF_VFR = Degree of social participation deficits in the aspect of VFR
Endogenous variables:
Psychological indicators (1: strongly disagree to 7: strongly agree)
SE1 = You are not a part of society
SE2 = You are unable to participate in social activities
SE3 = You are unable to access social resources and opportunities
92
SE4 = You have inadequate relationships with friends
SE5 = You have inadequate relationships with relatives
Endogenous (latent variables)
SE_SPO = Degree of social exclusion in terms of social participation and
opportunities
SE_SN = Degree of social exclusion in terms of social networks with friends and
relatives
4) Errors terms
e1, e2, …, en = Error influences for each variable
A) The group of latent variables
From Figure 7-12, the degrees of satisfaction with driving in various dimensions were grouped into 2
unobserved variables, which were the degrees of satisfaction with 1) convenience of driving (SF_PRI1:
D_Com and R_Safety) and 2) traffic conditions (SF_PRI2: Traffic_C and Parking_S). As in Figure 6-
9, the degrees of satisfaction with the use of ground public transportation were clustered into 2 groups,
including degrees of satisfaction with 1) service convenience (SF_PUB1: Space, Seat_A, S_Com,
Walkw_C and S_Safety) and 2) service operation (SF_PUB2: S_Info, S_Relia and S_Fre). However,
the degree of satisfaction with travel cost remained alone without being added into any group due to its
small weight according to factor analyses.
To construct the variables for socio-economic factors, walking, vision and hearing ability were grouped
as general health conditions for elderly people (Health_C). Health condition stood in the same layer
with degree of satisfaction with transportation because health condition might relate to not only
difficulty of transportation but also individual motivation to go out to do activities. In addition, number
of family members (Family_M) and degree of extroverted (D_Extroverted) were added in the SEM as
exogenous variables that might affect the overall process of feeling social exclusion. It has to be noted
that degree of satisfaction with travel cost and income level had no significant connection with any
grouped variable.
B) The group of mediated variables
For the mediated variables for SEM of both elderly drivers and public transport passengers, degrees of
social participation deficits (Mediators) referred to the gaps in number of existing and desired trips in
various kinds of social activities and were categorized into 2 groups, including social participation
deficits in the aspects of 1) social participation and opportunities (SPD_SPO: G_Shopping and
G_Hobby) and VRF (SPD_VFR: G_VFriend and G_VRelative). It is notable that the gaps in number
of trips for administrative tasks and going to hospital were not included in the models. This is because
these gaps were very small and not significantly related with any of the variable in the models. It should
be noted that gaps in duty activities (administrative tasks and going to hospital) and some socio-
economic variables had no significant connection with any grouped variable.
C) The group of endogenous variables
For the dimensions of social exclusion (endogenous variables) for both drivers and public transport
users, SE1, SE2 and SE3 were grouped into the degrees of social exclusion in dimension of social
participations and opportunities (SE_SPO) and SE4 and SE4 were clustered into the degree of social
exclusion in terms of social networks with friends and relatives (SE_SN).
93
Figure 7-12: Standardized SEM for the Process of Social Exclusion in Elderly Private Car Users
(Note: # is value fixed at 1, * is p<0.05; ** is p<0.01; and *** is p<0.001)
Figure 7-13: Standardized SEM for the Process of Social Exclusion in Elderly Public Transport Users
(Note: # is value fixed at 1, * is p<0.05; ** is p<0.01; and *** is p<0.001)
94
7.3.6 The process for feelings of social exclusion in elderly private car users
According to the SEM shown in Figure 7-12, which aimed for indirect effects of degrees of satisfaction
with convenience in driving and traffic conditions (SF_PRI1 and SF_PRI2, respectively) on the degrees
of social exclusion (SE_SPO and SE_SN) through degrees of social participation deficits (mediated
variables; SPD_SPO and SPD_VFR), SF_PRI1 had larger effects on SPD_SPO and SPD_VFR (-0.94
and -0.47, respectively) than those of SF_PRI2 (-0.23 and -0.2, respectively). Subsequently, the indirect
effects of SF_PRI1 on feelings of social exclusion (-0.62 and -0.55 on SE_SPO and SE_SN,
respectively) were also larger than those for SF_PRI2 (-0.19 and -0.24 on SE_SPO and SE_SN,
respectively). In addition, SF_PRI1 had a direct effect on SE_SP (-23). It can be implied that
unsatisfactory convenience in driving and traffic conditions discouraged elderly to drive to participate
in social activities, leading to social participation deficits in the aspects of both social participation and
opportunities, and VFR, resulting in higher degrees of social exclusion in both dimensions of social
participation and opportunities (SE_SPO) for social networking with friends and family (SE_SN).
However, it seemed that respondents with aged-related characteristics, such as those limited by health
conditions, tended to focus on convenience in driving rather than traffic congestion. According to the
interviews, traffic and parking congestion did not seriously discourage the elderly from going out to do
social activities because they could avoid travelling during peak hours and their schedules for non-duty
activities were more flexible than those who worked.
For the effects of mediators, the SEM indicated that SPD_SPO had a positive-mediated effect on SE_SP
(0.44), but no effect to SE_SN. Besides, SPD_VFR also had a slightly positive effect on SE_SP (0.40).
However, SPD_VFR had a relatively large effect to SE_SN (1.16). It can be implied that VFR was
related directly to feelings of social exclusion in terms of their social network with friends and relatives.
Based on the interviews, SPD_VFR had effects on both SE_SPO and SE_SN because some of
respondents did not only go out to meet their friends or relatives. They also did other activities, which
affected the degree of social exclusion in the dimensions of SE_SPO, such as shopping and hobbies.
The socio-economic factors (Health_C, Family_M and D_Extroverted) also had direct and indirect
effects on feelings of social exclusion. First, health conditions (Health_C) including vision (Vision) and
hearing (Hearing) ability had a slightly negative effect (-0.26) on the degree of satisfaction for
convenience in driving (SF_PRI1). It seemed that elderly people with poorer vision and hearing ability
felt more uncomfortable driving. In addition, respondents in poorer health tended to go out to do
shopping and hobbies with less frequency due to the direct negative effect of Health_C on SPD_SA (-
0.39). Second, the number of family members (Family_M) directly affected SE_SN (0.45). According
to the interview, respondents staying in larger family sizes tended to stay at home and do various
activities with their family member, resulting in less frequency of the VFR trips, leading to the lower
degree of social network with their friends and relatives. Third, degrees of extroverted (D_Extroveted)
referred to the level of preferring to go out to engage in social activity and affected SPD_VFR (0.38).
It can be implied that extroverted people, who expected a higher degree of social participation, tended
to have more desired trips, resulting in a larger gap in number of trips. Subsequently, a higher degree
of social exclusion is felt.
7.3.7 The process for feelings of social exclusion in elderly public transport users
Consistent with the SEM shown in Figure 7-13, there was no direct effect on degrees of satisfaction
with the use of public transportation (SF_PUB1 and SF_PUB2) on degrees of social exclusion (SE_SPO
95
and SE_SN). However, aimed for the indirect effects, it was obvious that the SF_PUB1 had much larger
effects on the mediators (-0.60 and -0.57 on SPD_SPO and SPD_VFR, respectively) than those of
SF_PUB2 (only -0.24 on SPD_SPO). Consequently, the indirect effects of SF_PUB1 on the degrees of
social exclusion (-0.53 and -0.50 on SE_SPO and SE_SN, respectively) were larger than those of
SF_PUB2 (-0.21 on only SE_SPO). Similar to private car users, it seems apparent that elderly people
with limiting health conditions tended to pay more attention to the convenience of use for public
transport compared to the aspect of service operation.
In terms of the effects of mediators, SDP_SPO had a greater effect on SE_SPO (0.88) and mediated
effect on SE_SN (0.42). Besides, SPD_VFR moderately affected SE_SN (0.43). Based on the
interviews, the reason that SDP_SPO had an effect on both SPD_SN and SPD_VFR was that some
elderly people not only went out to meet their friends and relatives, but also to do social activities with
their friends and relatives (same as the case of private car users).
The effects from socio-economic factors were similar to private car users, with elderly people in poorer
health in terms of walking, vision and hearing abilities feeling slightly more inconvenienced to use
public transport according to the positive effect of Health_C on SF_PUB1 (0.11). Besides, health
conditions had a slightly negative effect on SPD_SPO (-18). It seems that public transport users who
had poorer health conditions tended to go out to do shopping and hobbies with less frequency (same as
the case of private car users). Interestingly, it was found that health conditions also affected the degree
of extroverted (D_Extroverted). It may be that respondents in poor health felt more uncomfortable going
out, leading to a lower degree of extrovert. In this case, D_Extroverted slightly affected SPD_VFR
(0.16) for the same reason as private car users. In addition, D_Extroverted was also influenced by the
number of family members (Family_M). According to the interviews, respondents living in larger
families were able to join in on home activities with their family members more often, resulting in less
passion to go out. The result is a lower degree of social exclusion in the dimension of SE_SPO due to
the positive effect of Family_M to SE_SPO (0.16).
7.3.8 The approach to reduce the feelings of social exclusion
The results of SEM for both elderly private car and public transport users were corresponding with the
result of the analysis by using the regression analysis method (count data and logit model). However,
by using SEM for the analysis, the direct effect of the explanatory (exogenous) variables were found
that was different from using the regression analysis method. In additional, all relationships among all
groups of variables were analysed simultaneously, which is the advantage of SEM.
The SEM indicated that unsatisfactory transportation led to social participation deficits, consequently
resulting in feelings of social exclusion in the dimension of inability to participate in social activities
and opportunities as well as the lack of social networking with friends and relatives. In order to decrease
feelings of social exclusion among the elderly in Bangkok, elderly people should be encouraged to go
out more frequently for non-duty activities including shopping, hobbies and VFR, as indicated by the
results. According to the SEM, a possible approach to encourage the elderly to go out more frequently
is to increase the degree of satisfaction with daily transportation. However, the relative improvement of
overall aspects for public transport services might not be possible during recent years due to the limited
budget of the government. Therefore, this study aimed to recommend the important points that should
be improved first, as implied by the SEM in Figure 7-12 and 7-13, based on the prioritization of
magnitudes for the effects of degrees of satisfaction with transportation on degrees of social exclusion,
96
combined with the aspects of transport performance that were not satisfactory to the elderly (aspects
that had a score less than 4 observable from Figure 7-9 and 7-11). According to Figure 7-12 and 7-
13, the model indicated that the aspect of convenience in driving and the use of public transport had the
largest magnitudes of effect on the degree of social exclusion. Nevertheless, it should be noted that the
recommendation of policy implementations to improve the convenience of driving, which are related
to health conditions and road safety, may require further research. Such study would focus on an
approach to improve the degree of satisfaction for convenience of use in ground public transport, which
had approximately two-thirds of overall travel demand.
According to Table 7-9 and Figure 7-10, access walkway conditions to the station were likely to be a
problem for elderly people with limiting health conditions, particularly metro users with longer access
walking distances (578.96 meters) than those of transit users (303.03 meters). For this range of metro
access distance, it is possible to introduce a feeder system, such as a small bus or van, to transport
elderly people to metro stations.
According to Figure 7-9, space in vehicle and seat availability was not satisfactory for both transit and
metro users. A potentially simple approach is to increase the number of priority seats, which could be
done without a substantial impact on service performance. When no elderly passengers are present,
other passengers can sit in those seats. Based on the interviews, the comfort level of metro service was
relatively satisfying for elderly people because more facilities in metro services were likely to be
provided than those for transit. Besides, the comfort of transit service was not likely to be satisfying for
elderly people yet. As reported by respondents, additional support facilities for elderly bus and van
users should be more provided at both waiting areas and in vehicles, such as hand rails, priority seating
at waiting areas, shaded areas, and better seat quality, especially for the routes used to engage non-duty
activities and locations (shopping, hobbies and VFR) such as parks, gathering places, elderly clubs,
community cafés and major department stores.
In terms of the aspect of operation, which has less effect on feelings of social exclusion, it would be
beneficial if this aspect were enhanced as well, after the improvement of the convenience aspect.
According to Figure 7-9, punctuality and service frequency for both metro and transit operation are not
likely to be a huge problem for the elderly. The problem is that the system of information for transit
services seems difficult for the elderly to use. As reported by respondents, insufficient information about
service operation for bus and van transport was provided at the bus and van stops. For instance,
respondents could only gain information about the number of buses at the bus stops. Other important
operational information should be provided as well, such as route maps and time tables, especially in
the waiting areas along the routes. In addition, travel assistance systems that refer to care takers who
support elderly people and their use of public transportation should be introduced at the major metro
and transit stations along the routes that have a high potential to link them to non-duty activities places,
which would better support the elderly for travel convenience and information.
97
CHAPTER 8 ELDERLY CARPOOL SUPPORT PROGRAM BY
NEIGHBORHOOD DRIVERS IN THE AREA WITH POOR
ACCESSIBLE TRANSPORTATION
8.1 Methodology
From Chapter 7, it was found that some elderly people lived in the aear with poor acess to both public
transportation and paratransit (e.g. taxi and tuk tuk). Those elderly tended to feel uncomfortable to drive,
leaind them to the difficult to go out to participate in socia activities. They may be looking for
independent transportation options to go out and participate in community activities. Therefore, this
part investigated the needs of elderly to use carpool elderly support service from neighborhood drivers
who lived in the same town or village with them. This study also examines if trip frequency increases
for participation in community activities among the elderly, and whether the intensity of feelings of
social exclusion of elderly in terms of feeling apart from society and feeling unable to access social
services and opportunities were reduced because of this carpool service. This part tried to define the
appropriate acceptable price of the volunteer service by applying KLP method in the case that fees per
ride was required to defray the costs of those neighborhood carpool drivers. The methodology of this
section is written below.
8.1.1 Data collection
To collect the data for the present study, Bangkok was selected because this city has the largest
population in Thailand. Data was collected in suburb areas of Bangkok, which are Bang Khun Thian,
Bang Na, Min Buri, Don Mueang and Bang Khae as shown in Figure 8-1. There was a high change to
meet the targets of respondents living in the areas that was difficult to access to public transport and
para transit. The target respondents were divided into two groups as follows: 1) elderly Bangkok
residents aged 60 and over who might need help from volunteers for transport and 2) general Bangkok
residents who could volunteer to respond to the needs of the elderly. The questionnaire mainly asked
elderly people if they needed or desired transportation services from volunteers, and it also asked
general residents if they were interested in volunteering to support the elderly in these programs. In
May 2017, 103 elderly residents and 106 other general residents who were interviewed (the full
questionnaire (both English and Thai version) and survey pictures are shown in Appendix B-2).
A) The question for Bangkok elderly
The questionnaire asked 103 elderlies, “Do the elderly need the residents to provide them carpool
support service to support their travel needs?”. In addition, to investigate what reasons in terms of
transport difficulty made the elderly request this carpool service, the participants were asked to report
their current daily transport situation, including the uses of private car, paratransit and public
transportation. In the case that fees per ride is required, to determine the perception of seniors to the
service price levels based on The KLP approach, the elderly people were asked to rate suitable prices
per ride for using the volunteer services, including:
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1. Reasonable: the price at which they thought it was reasonable to use the service.
2. Expensive: the price at which they though it was expensive to use the service.
3. Too expensive to be willing to purchase: the maximum price they were willing to pay for using
the service.
4. Too inexpensive to be willing to purchase: the minimum price below which they were not
willing to use the service because they doubted the quality or safety of the volunteer service.
Questions were also asked among elderly people about their trip frequencies and their feelings of social
exclusion concerning the current situation and the hypothetical situation in which they were supposed
to be supported by volunteers.
Figure 8-1: Survey locations
B) The question for Bangkok residents
To investigate the amount of potential supply (Bangkok residents who were willing to support elderly),
the questionnaire asked 106 residents if they were interested in supporting the elderly living in the same
town or village with them in the carpool program as follows.
Case 1: During workday, picking up and sending elderly along their commuting route
Case 2: During holiday, picking up and sending elderly even not along their commuting route
Case 1 refers to the case in which the volunteer provides service to the elderly along the same route that
they generally commute or travel, and Case 2 refers to the case in which the volunteer assists seniors
even along other different routes than they commonly use. Subsequently, the potential carpool drivers
were asked to state the amount of money they need to defray their costs of volunteering, such as fuel,
transit fares and dedicated efforts, but not any profit.
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8.1.2 To investigate which factor affecting the decision of elderly to use the carpool service, and
the decision of Bangkok residents to support elderly
To estimate which factor affecting the decision to join the carpool support service, a binary regression
analysis was developed. The outcome, independent variable: y, of the binary logit model was the
decision of elderly the use the service and decision of Bangkok residents to support elderly as below.
1 if the elderly i was interested in using carpool service from resident
yi (elderly) =
0 if the respondent i was not interested in using carpool service from resident
1 if the resident i had the motivation to support elderly in carpool service
yi (resident) =
0 if the respondent i had no the motivation to support elderly in carpool service
For elderly, the explanatory variables (x) were their socio demographic, such as age and gender; their
health condition, such as walking and hearing ability; their living condition, such as family size and
income level; and their current transportation situation, such as the driving ability and ability to access
public transportation service and paratransit, ranked from 1 (very poor) to 5 (excellent).
For resident, the explanatory variables (x) were the price of the service in JPY per kilometer, free time
in hours per day during the week and on weekends, anxiety levels in terms of their own safety while
servicing unknown elderly, ranked from 1 (having no anxiety) to 5 (having complete anxiety), and the
confidence level in their skills for providing elderly support ranked from 1 (completely unconfident) to
5 (completely confident). The probability and the utility function of the binary logit model are expressed
as Equation 8-1 and 8-2, respectively (Ben-Akiva and Lerman, 1985).
𝑷𝒊 =𝒆𝑼𝒊
𝟏 + 𝒆𝑼𝒊 (7-1)
𝑼𝒊 = 𝜷𝟎 + 𝜷𝟏𝑿𝒊𝟏 + 𝜷𝟐𝑿𝒊𝟐 + ⋯ + 𝜷𝒏𝑿𝒊𝒏 + 𝜺 (7-2)
Where;
Pi : the probability that the event that personi will use the carpool service (for elderly),
and support elderly in carpool service (for resident)
Ui : utility function for personi,
Xi1, Xi2 ,…, Xin : the value of each independent variable,
β0 : y-intercept or the expected value of Ui when all xi are zero,
β1, β2,…,βn : coefficients for Xi1, Xi2,…, Xik, receptively, and
ε : error value that is expected to be zero.
8.1.3 To examine the amount of willingness to pay by KLP method
In the case that fees per ride is required, by applying The Kishi’s Logit (KLP), four relative cumulative
frequencies of service prices rated by elderly (reasonable, expensive, too expensive to be willing to
purchase, and too cheap to be willing to purchase) were regressed using the continuous function of the
binary logit model as shown in Equations (8-3) and (8-4). These functions were used to determine
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acceptable price ranges and standard prices, as shown in Figure 3-6 (Kishi and Sato, 2005).
𝑻𝒊(𝒕) =𝟏
𝟏 + 𝐞𝐱𝐩 (𝒂𝒙 + 𝒃) (8-2)
𝑭𝒊(𝒙) = 𝒂𝒙 + 𝒃 (8-4)
where:
T i : Relative cumulative frequency (i is 1 to 4)
F T 11 ; : Should be less expensive as shown in Figure 3-9 (d)
F T 22 ; : Should be more expensive as shown in Figure 3-9 (d)
F T 33 ; : Too expensive to be willing to use the service as shown in Figure 8-2
F T 44 ; : Too cheap to be willing to use the service as shown in Figure 8-2
x : Price of the volunteer service
a, b : Coefficients
8.1.4 To define the appropriate service price that could cover whole demand of elderly people
In the case that fees per ride is required, to define the appropriate price at when the amount of supply
(volunteer) can cover the whole demand (elderly who need to use the volunteer service), this section
investigates whether or not the supply of volunteers can cover the demands of the elderly at an
acceptable price based on the perception of demand, as analyzed by KLP. The study estimated the
supply-demand ratio (S/D) using Equations (8-5) to (8-7). If S/D is less than 1, the supply is inadequate
to support demand, and vice versa. Finally, the framework of the analysis including variable, path of
the relationship and analysis method is shown in Figure 8-3.
Figure 8-2: The price indicator references of KLP (Kishi and Sato, 2005)
101
𝑺/𝑫 =𝑸𝒔𝒖𝒑𝒑𝒍𝒚
𝑸𝒅𝒆𝒎𝒂𝒏𝒅 (8-5)
𝑸𝒔𝒖𝒑𝒑𝒍𝒚 = ∑(𝑷𝑺𝟏 × 𝑷𝑺𝟐 × 𝑷𝑨𝑮𝑬𝒊× 𝑷𝑶𝑷𝑨𝑮𝑬𝒊
× 𝑷𝑶𝑷𝑫𝑰𝑺𝑻𝑹𝑰𝑪𝑻𝒊)
𝒏
𝒊=𝟏
(8-6)
𝑸𝒅𝒆𝒎𝒂𝒏𝒅 = ∑(𝑷𝑫 × 𝑷_𝑨𝑮𝑬𝒊 × 𝑷𝑶𝑷𝑨𝑮𝑬𝒊× 𝑷𝑶𝑷𝑫𝑰𝑺𝑻𝑹𝑰𝑪𝑻𝒊
𝒏
𝒊=𝟏
) (8-7)
When:
𝑺/𝑫: Supply-demand ratio
𝑸𝒔𝒖𝒑𝒑𝒍𝒚: Total supply (people)
𝑸𝒅𝒆𝒎𝒂𝒏𝒅: Total demand (people)
𝑷𝑺𝟏: The proportion of Bangkok residents who had the motivation to support elderly
𝑷𝑺𝟐: The proportion of residents, willing to support elderly in carpool service, determined by the binary
logit model of amount of supported money from servicing elderly
𝑷𝑫: The proportion of elderly requesting carpool support services
𝑷_𝑨𝑮𝑬𝒊: The proportion of the number of samples in each age range (years)
𝑷𝑶𝑷𝑨𝑮𝑬𝒊: Total number of Bangkok population in each age range (years) according to the Official
Statistics Registration Systems (2017)
𝑷𝑶𝑷𝑫𝑰𝑺𝑻𝑹𝑰𝑪𝑻𝒊: Total number of Bangkok population in each district (people) according to the (BMA
Data Center, 2017)
Figure 8-3: The structure of the analyses
8.2 Data and Results
8.2.1 Descriptive statistic
The questionnaire asked Bangkok elderly and residents about their sociodemographic information,
current transportation mode and transport situation. The summary of descriptive statistic of Bangkok
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103 elderly who aged 60 or over 60 years old, and 106 residents who aged lower than 60 years old is
shown in Table 8-1. For the sample of elderly, the average age of elderly was around 70 years old and
the proportions of male and female were mostly similar. Elderly of this sample set tended to live
together with others (average 4.1 person per house) and most of them had their own vehicle with driving
license. Elderly felt anxiety to stranger than residents.
Table 8-1: Descriptive statistic of Bangkok 103 elderly and 106 residents
Attribute Elderly Resident
Sociodemographic Average
Age (year) 69.3 36.9
Gen (% of male) 51.5 45.5
Monthly income (JPY per month) 59966.2 128393.2
Vehicle ownership with driving license (% of who owned their vehicle) 57.3 74.4
Family size (person) 4.1 3.2
Anxiety to stranger (1 = not really feel to 5 = really feel) 3.6 2.4
Confident level with supporting driving skill (1 = 0 to 5 = yes) NA 3.2
Free time during workday (hour per day) NA 2.5
Free time during holiday (hour per day) NA 3.4
Current transportation mode (%)
Non-motorize 7.8 6.6
Private vehicle 33.0 64.1
Transit 4.9 13.6
Metro 3.9 12.6
Paratransit 3.9 5.8
Relying on others 47.6 0.0
Current transportation situation
Driving ability (1 = very poor to 5 = very excellent) 2.9 4.1
Walking ability (1 = very poor to 5 = very excellent) 2.5 4.5
Difficult level to call paratransit from living area (1=easy to 5=difficult) 4.7 4.2
Distance from home to the nearest public transportation station (meter) 2338.8 2515.3
The need of support from others during doing activity outside home
(1 = no to 5 = yes) 2.5 1.3
From Table 8-1, Elderly who lived in the area that data was collected tended to experience the difficulty
to access the public transport service due the long walking distance from their home to the station
(average around 2.4 kilometer), and to call paratransit, such as taxi or tuk tuk. Very few elderlies could
access to public transportation, just 4.9%, 3.9% and 3.9% for transit metro and bus, respectively. In
addition, around half of them still had difficulty in walking and driving, but 33% of them still had to
drive to go out because public transport was difficult to access from their living area. Portion of elderly
needed support from others during doing activity outside home. Thus, those elderly (around 47%) also
tended to rely on others for transportation to go out for doing daily activity because they could get
assisted from others, such as holding the stuff and taking care while walking, during doing activity
outside home.
For the sample of Bangkok resident collected from the same place with elderly, the average age of them
was 36.9 years old. The proportion of female was slightly larger than those of male. They got average
income than those of elderly. They still had free time during workday, but it was less than those of
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holiday. Their driving ability was higher than those of elderly and most of them also had to rely on their
private vehicle to go out because public transport was difficult to access from their living place.
However, it was obviously seen that these residents, who were younger than elderly and had better
physical condition, did not have to rely on others while going out.
8.2.2 The willingness to join the elderly carpool service program
A) The decision to join the elderly carpool support program
The question asked both Bangkok elderly and resident about the condition of their living area
(considered as the origin of trip), the activity destination of elderly, and the working destination of
resident where they commonly commute during working day. The origin and destination (O-D) of them
is shown in Figure 8-4. It can be seen that their origins were from the suburb area of Bangkok, such as
Min Buri and Bang Khun Thien, where the public transport service was not likely to be access from
some living areas. Their travel destinations were quite similar that they tended to travel to the city area,
such as Pathum Wan or Bang Sue for their daily activity and working.
The question also asked about their willingness to join this elderly support carpool program. Table 8-2
shows the number of respondents willing to join the carpool support program. From Table 8-2, the
result shows that around one-third of elderly preferred using this carpool service during weekday and
half of them wanted to use the service during weekend. As mentioned earlier in Chapter 4, for residents,
this question was divided into two cases which are
Case 1: Are you willing to support elderly along your commuting route during your workday?
Case 2: Are you willing to support elderly even on your holiday and not in non-commuting route?
Figure 8-4: Origins (blue locations) and destinations (red locations) of trips of respondents
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Table 8-2: The number of respondents willing to join the carpool support program
Description
Weekday
(Case 1)
Weekend
(Case 2)
Person % Person %
Supply
Willing to support elderly for free (Volunteer) 47 44.34 4 3.77
Willing to support elderly but with requested supported
money (Paid volunteer) 4 3.77 29 27.36
Elderly
Preferred using carpool service 37 35.92 50 48.54
The reason for elderly who did not prefer using
carpool support service Person %
Preferred going out with family or neighborhood 21 20.4%
Preferred the government to improve transport services 32 31.1%
The data indicated that almost half of residents living in the same area with elderly people were willing
to pick elderly up and send them along their commenting route for free during their workday (mostly
from Monday to Friday), because they did not have to pay any extra travel cost, which can be considered
as volunteer. Based on the data the amount of supply (resident supporting elderly) was likely to be
adequate on weekday. However, around one-third of residents were motivated to support elderly on
holiday, but most of them requested the small amount of supported money from elderly for defraying
their travel cost (e.g. fuel cost) but it was not for their own profit because they had to support elderly
not in their commuting route that cost their extra travel cost. The amounts of supported money for Case
1 and Case 2 when residents were willing to support elderly during holiday are shown in Figure 8-5.
Figure 8-5: The amount of willingness to support elderly in carpool service of Bangkok resident
B) The reason to join the elderly carpool support program
To investigate the reasons and which factors affecting the decision to join the elderly carpool
support service of respondents. The binary logit models of the decision to join this carpool
program were developed for both elderly (y = 1 is use and y = 0 is not use) and residents (y =
1 is support and y = 0 is not support) as shown in Table 8-3.
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Table 8-3: The binary logit model of the factor affecting the decision to join the carpool program
Factors Coef. P>z
Elderly who wanted to use carpool the service
Difficult level to call paratransit from living area (1=difficult to 5=easy) -1.613 ***
Distance from home to the nearest public transportation station (meter) 0.003 ***
The need of support from others during doing activity outside home
(1 = no to 5 = yes) -1.587 *
Anxiety to stranger (1 = not really feel to 5 = really feel) -2.509 *
Driving ability (1 = very poor to 5 = very excellent) 0.777 *
Family size (person) -0.877 *
Constance 4.424
Pseudo R-square/ BIC 0.824/59.670
Resident who was motivated to support elderly on workday
Anxiety to stranger (1 = not really feel to 5 = really feel) -0.621 ***
Confident level with supporting driving skill (1 = 0 to 5 = yes) 1.008 ***
Constance -1.735
Pseudo R-square/ BIC 0.399/97.595
Resident who was motivated to support elderly even on holiday
Free time during holiday (hour per day) 1.119 **
Anxiety to stranger (1 = not really feel to 5 = really feel) -1.259 **
Confident level with supporting driving skill (1 = 0 to 5 = yes) 1.214 *
Supported money from servicing (1 = Available, 0 = Not available) 3.181 ***
Constance -8.473
Pseudo R-square/ BIC 0.767/49.243
From Table 8-3, it seems that elderly needed the carpool service because paratransit was difficult to
call from their living area; waking distance from their house to the station was far; and they experience
the difficulty of driving. However, from Table 8-2 and 8-3 elderly who needed the support from
others during doing activity outside home were not interested in using the carpool service because most
of them tended to go out with their friends or family members, who already supported them for travel
needs. From Table 8-2, another reason was that they preferred the government to improve transport
services for them instead of using carpool service from residents. It has to be noted that elderly who felt
anxiety about the service from stranger also tended not to select using carpool service from the person
they were not really familiar with even staying in the same village, according to the negative relation
of the variable of anxiety to the outcome.
The models of resident in Table 8-3 shows that not only the anxiety to stranger of elderly but also this
concern of residents affected the decision to join the carpool program. Residents also felt worry if they
had to service elderly who they were not familiar with according the negative relationships to the
outcome of the parameters of anxiety. In addition, residents who were not confident in their supporting
driving skill felt more hesitate to join the program. Who had more confident in their support skill tended
to join the program due the positive coefficient of this parameter. During holiday, the free time of
residents had the effect to the decision to spend their time to support elderly. As mentioned before, the
model indicated that more motivated residents would be willing to support elderly on holiday if they
could get the supported money from servicing elderly because they had to pay extra travel cost to
support elderly in non-commuting route, according to the positive coefficient of this parameter.
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8.2.3 The amount of willingness to support elderly of residents
From Figure 8-5, most of residents were willing to volunteer on their commuting route during their
workday but most of them requested the supported money for defraying their extra travel cost during
supporting elderly on holiday. Thus, it is important to predict the number of motivated resident, who
would be willing to support elderly on holiday in each different service price range. Therefore, the
binary regression model of the amount of willingness to support elderly, based on Equation 8-1 and
8-2, was developed as illustrated by Equation 8-8 and 8-9. From the model the coefficient of variable
of amount of support money (X) had positive relationship to the decision to support elderly. There, it
can be implied that more residents would be willing to support elderly in carpool service when they
could get higher amount of supported money for defraying their travel cost. Actually, volunteering is
unpaid by definition. Nevertheless, residents willing to support elderly on their holiday can be called
‘paid volunteering’ because they requested to be paid to for their extra travel costs.
𝑷𝒊 =
𝒆𝑼𝒊
𝟏 + 𝒆𝑼𝒊
(8-8)
𝑼 = −𝟒. 𝟏𝟒𝟗 + 𝟎. 𝟏𝟎𝟔𝑿 (Pseudo R2 = 0.335, P-value of X = 0.001) (8-9)
where:
𝑷𝒊 : The probability that residenti was willing to support elderly
𝑼𝒊 : Utility value for residenti
X : Amount of supported money (JPY per kilometer)
8.2.4 The service price perception of elderly for using carpool service
During using the service on holiday, while supply (resident) requested the supported money from
elderly for defraying their travel cost, it was important to identify how elderly perceived the carpool
service price. Thus, KLP method (based on Equation 8-3 and 8-4) was applied to examine the service
price perception of elderly as shown in Equation 8-10 to 8-14, Then, the four prices indicators are
illustrated in Figure 8-6.
𝑻𝒊(𝒕) =
𝟏
𝟏 + 𝐞𝐱𝐩 (𝒂𝒙 + 𝒃)
(8-10)
𝑻𝟏; 𝑭𝟏 = −𝟎. 𝟐𝟒𝟒 + 𝟓. 𝟒𝟐𝟎𝑿 (R2 = 0.962) (8-11)
𝑻𝟐; 𝑭𝟐 = 𝟎. 𝟏𝟗𝟖 − 𝟓. 𝟒𝟑𝟎𝑿 (R2 = 0.981) (8-12)
𝑻𝟑; 𝑭𝟑 = −𝟎. 𝟏𝟑𝟗 + 𝟓. 𝟓𝟕𝟎𝑿 (R2 = 0.965) (8-13)
𝑻𝟒; 𝑭𝟒 = 𝟎. 𝟑𝟔𝟔 − 𝟒. 𝟖𝟗𝟕𝑿 (R2 = 0.969) (8-14)
where:
𝑻𝒊 : Relative cumulative frequency (i is 1 to 4)
𝑻𝟏; 𝑭𝟏 : Should be less expensive as shown in Figure 3-9 (d)
𝑻𝟐; 𝑭𝟐 : Should be more expensive as shown in Figure 3-9 (d)
𝑻𝟑; 𝑭𝟑 : Too expensive to be willing to use the service
𝑻𝟒; 𝑭𝟒 : Too cheap to be willing to use the service
X : Price of the carpool service (JPY per kilometer)
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Figure 8-6: Price indicators analyzed by KLP method
From Figure 8-3, the maximum price was 32.59 JPY per kilometer when more elderly would not be
willing to use the service if the price goes beyond this level. If the price goes lower than 16.19 JPY per
kilometer, most of elderly would doubt about the quality and safety of the service because it is too cheap
for them. Based on the price indicators, the service price should be set at the standard price, 24.55 JPY
per kilometer. However, it was necessary to investigate if the amount of supply (motivated residents
willing to support elderly on holiday) could cover amount of supply (elderly willing to use the service),
which will be discussed in the next section.
8.2.5 Pricing and policy implication
This section investigates whether or not the supply of motivated residents on holiday can cover the
demands of the elderly at an acceptable price based on the perception of demand, as analyzed by KLP
and shown in Figure 8-6. The study estimated the supply-demand ratio (S/D) using Equations 8-5 to
8-7 from Chapter 4. If S/D is less than 1, the supply is inadequate to support demand, and vice versa.
The simulations of S/D at each price indicator are shown in Table 8-4.
In Table 8-4, not only four price indicators from KLP but also the trial price, where S/D is exactly equal
to 1, were applied in to the simulation. The simulation indicates that the maximum service price gave
the ratio of S/D greater than 1, but the S/D ratio at the standard price was just 0.75, which was smaller
than 1. It was implied that the amount of supply cannot cover whole demand at the standard price. Thus,
the trial price, where S/D will be equal to 1, was calculated. It was found the supply can cover demand
at 28.0 JPY per kilometer (service equilibrium price). However, the elderly will consider this price is
high because it is higher than the standard price according to Figure 3-6 in Chapter 3. In this case, the
government can subsidize the service by 3.4 JPY per kilometer to make the perceived cost sufficiently
affordable for elderly.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0.00 7.00 14.00 21.00 28.00 35.00
Cu
mu
lati
ve
freq
uen
cy
Price (JPY/Kilometer)
Should be less expensive
Should be more expensive
Too expensive to buy
Too cheap to buy
108
Table 8-4: Simulations of the supply-demand ratio at price indicators analyzed by KLP and the trial
price where S/D is equal to 1
Price indicator Price S/D
Maximum 32.6 1.42
The price where S/D = 1 28.0 1.00
Standard 24.6 0.75
Reasonable 20.7 0.53
Minimum 16.9 0.37
Alternatively, based on the factor affecting the decision to support elderly of motivated residents from
Table 8-3, instead of pricing strategy, the Bangkok government can also encourage them to support
elderly by reducing the anxiety level of them concerning their personal safety when they provide
services to unknown elderly. Registration and database systems can be created to increase the reliability
of the volunteer system, resulting in reduced anxiety levels. In addition, increasing confidence in elderly
support skills by holding workshops to train residents and provide basic knowledge of how to support
the elderly through carpool program can persuade more residents to service elderly.
8.2.6 The improvement of trip frequency and the reduction of feelings of social exclusion
The last important aspect needed to be confirmed was that if the carpool support service could improve
the trip frequency and reduce the degree of feelings social exclusion of elderly. Thus, questions were
also asked among elderly people about their trip frequencies and their feelings of social exclusion
concerning the current situation and the hypothetical situation in which they were supposed to be
supported by volunteers, as shown in Figure 8-7. The one-group statistical t-test technique was applied
in order to notice the significant change in means of trip frequency and degree of social exclusion, at
between current situation and the hypothetical situation as shown in Table 8-5.
Figure 8-7: Trip frequency and degree of feeling social exclusion at current situation and hypostatical
situation (when carpool service is available) of elderly respondents
where:
TRIP_F1 : Trip frequency to do recreational activity, such as VFR (visiting friend
or relative), hobby and leisure (time per week)
TRIP_F2 : Trip frequency to do administrative activity, such as documentary tasks
and going to hospital (time per week)
12
34
5
With_carpool Without_carpool
TRIP_F1 SE1
01
23
45
With_carpool Without_carpool
TRIP_F2 SE2
109
SE1 : Degree of feeling social exclusion in term of being apart from
community, social activity and relationship with others (1 = strongly
disagree to 5 = strongly agree)
SE2 : Degree of feeling social exclusion in term of being inaccessible to social
resources, services and opportunities (1 = strongly disagree to 5 =
strongly agree)
With_carpool : At hypostatical situation when carpool service is available
Without_carpool : Current situation
Table 8-5: Mean comparisons analysed by statistical T-test of trip frequency and degree of feelings
social exclusion
Item With_carpool Without_carpool P>t
TRIP_F1 2.389 1.592 0.001
TRIP_F2 0.259 0.224 0.734
SE1 3.148 4.041 0.001
SE2 3.778 3.837 0.724
From Table 8-5, the results show that recreational trip frequency, referring to trips and outings for
leisure activities such as shopping, hobbies and visiting friends, increased significantly by 0.797 times
per week if they were supposed to be supported by carpool service. However, administrative trip
frequency, which refers to mandatory trips such as for documentary tasks and going to the hospital, did
not increase significantly. Based on the interviewed, this is likely because this kind of activity is more
like a necessity or duty rather than leisure, which the elderly did not want to do according to the
interviews. In addition, the data also indicated that the degree of feelings of social exclusion in terms
of feeling apart from society (SE1) significantly reduced by having carpool service, but the degree of
feeling unable to access social resource, service and opportunity (SE2) did not significantly creased due
to the analysis. This decrease of SE1 had significant Pearson correlation with TRIP_F1 by 0.84 with
0.91 coefficient.
It can be implied that the implementation of carpool service can achieve the goal that it would benefits
to the fulfillment of recreational trip frequency of elderly people. According to the pricing strategy
mentioned earlier, when elderly people are willing to pay to use the service and travel more frequently
to participate in more recreational activity in their community. Subsequently, they feel less being apart
from their community.
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CHAPTER 9 CONCLUSION AND RECCOMENDATION
This chapter describes the conclusion and recommendation of the study. The aim of this entire study is
to address the issue of social exclusion caused by transportation difficulty of two case studies of 1) low-
income and 2) elderly populations, by encouraging them to participate in more society by proposed
transportation policy, which were not included in the low mobility transport support plan of the Ministry
of Transportation of Thailand. The conclusion of this research is divided into parts, which are the
summary of the studies, policy implication, research contribution and recommendation, respectively.
9.1 Summary
According to the objective of the study, this study clarified the current issue of social exclusion caused
by transportation difficulty of Bangkok low income and elderly people. The findings provided the
evidence that the proposed transportation policy and implication in this study could support those people
to make travel more frequently to participate in more social activities, services and opportunities, and
subsequently their degree of feeling social exclusion reduced. Finally, the summaries of the study of
each focused group are shown below.
9.1.1 Public transport subsidy to reduce social exclusion of Bangkok low income
This section assessed the effect of free train policy on reducing social inclusion of Bangkok low-income
population by investigate the relationship between 1) the ability to make more trip participate in more
social activity and feel less social exclusion, and 2) the advantage of travel cost saving after using free
train by applying binary logistic regression method. In addition, the efficiency of the distribution of free
train policy to the target group (low-income people) was evaluate by calculating the error of inclusion.
After using free train, 32.65% of passengers traveled more frequently to join more social activities,
which were travelling to their hometown, visiting friend or family, doing business and recreational
activities, and subsequently they tended to feel less degree of social exclusion in the dimension of being
not able to access social opportunities, services and activities. These increase of trip frequency and
decrease of degree of feeling social exclusion were significantly correlated by 0.77 pearson correlation.
This result is corresponding with the previous studies of the effect of public transport subsidy on the
increase in trip frequency of users (e.g. Goeverden et al., 2006 and Witte et al., 2006).
However, the distribution of free train policy to the target group (low income users, intended benefit)
was not likely to be completed with 40.31% of non-low-income users (unintended benefit). The results
showed that the beneficial group was low-income, especially person with monthly income less than
22,292 JPY, who tended to make more trip and feel less social exclusion after using free service rather
than non-low-income users did. To remove those non-low-income users the registration of the specific
card given to low income people to use free service was recommended.
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9.1.2 The investigation of social exclusion caused by current transportation difficulty of
Bangkok elderly
This part aimed to investigate what aspects of transport performances were unsatisfied by Bangkok
elderly people and caused feeling social exclusion, as well as recommend hot to improve those
unsatisfied performances. This part examined the degree of feeling social exclusion caused by the
difficulty of the use of private car and public transportation of elderly people by investigating the
significance of the relationship among 1) degree of satisfaction with transportation, 2) the gaps in
number of trips and 3) the degree of feeling social exclusion by applying count data regression, ordered
logit model and structural equation modelling (SEM) approaches. This topic has not been studied in
detail in Thailand, a Southeast Asian country. The study highlights the application of gaps in the number
of existing and desired trip frequency to measure the degree of social participation deficits and
psychological indicators, which are utilized to assess the level of social exclusion.
In recent years, elderly people in Bangkok have become more comfortable driving but still experienced
the difficulty of driving. In order to travel and participate in social activities, older people have paid
more attention to the aspect of transport convenience rather than traffic conditions or public transport
service operation. However, convenience in the use of public transit and the metro showed low levels
of satisfaction, particularly by elderly people with restrictive health conditions that limited mobility.
The analysis also confirms that elderly people with poor health conditions have more difficulty driving
and using public transport, which is corresponding of the results from the previous studies (Engels and
Liu, 2011; Ibeas et al., 2014). The results indicate that elderly car users still were likely to be able to
make more frequent trips than those who used public transport.
Although mandatory activities tended to be considered more essential for other age groups, shopping
and non-mandatory activities had more influences on the feelings of social exclusion of Bangkok’s
elderly. However, to access shopping and non-mandatory destinations, a large proportion of the elderly
needed social assistants to support them during travel; this help was difficult to acquire because of the
temporal mismatch between the availability of the elderly and their assistants. Therefore, most of the
elderly had to travel using their own transportation.
However, according to the model, dissatisfaction with daily driving and the use of public transport
discouraged the elderly from going out, generating the gaps in the number of trips between existing and
desired trip frequency taken by seniors to engage in non-duty activities such as shopping and VFR,
leading to social participation deficits that subsequently result in feelings of social exclusion for the
dimensions of social participation, opportunities and social networking with friends and relatives, which
is the same trend with the previous study of Currie et al. (2010). Nevertheless, the most serious
dimension was that Bangkok’s elderly did not feel they were part of society, especially who used taxi
for daily transportation.
Besides, according to the SEM, dissatisfaction with the aspects of transportation also had a direct
influence on feelings of social exclusion by increasing the degree of social exclusion. In addition,
different socio-demographic characteristics among the elderly included health conditions, the number
of family members and degree of assertiveness, which also both direct and indirectly affected the level
of social exclusion. First, elderly people in poor conditions of health tended to have larger gaps in the
number of shopping and leisure trips, resulting in higher feelings of social exclusion. Second, elderly
people living with a large family tended to feel less social exclusion for the aspect of social participation.
112
Last, extroverted elderly people with more passion to go out tended to have larger gaps in the number
of VRF trips and consequently felt less social exclusion.
In order to combat and decrease the degree of social exclusion, one possible approach may be to increase
the level of satisfaction for public transportation. To promote social inclusion among the elderly, the
gap between the numbers of desired and existing shopping and non-mandatory trips should be reduced.
However, the results indicate that aspects of convenience for the use of public transportation should be
the first priority for improvement before service operation, which is corresponding with the previous
study about the improvement of public transportation for encouraging elderly to be more willing to
travel of Wong et al. (2018).
However, The analysis from 201 samples could mostly represent the whole Bangkok elderly because
the age distribution of collected sample was relatively similar to those of Official Statistics
Registration Systems (2016) (see Appendix C for the chi-square test). However, in terms of the
limitation, the proportion of gender of the sample was not relatively similar to those of population but
the effect of gender was not focused in the analysis of the paper.
It should be noted that this study took place in Bangkok, Thailand. Therefore, it is possible that the
results may differ if the study method is replicated for transportation systems and urban planning in
other South global countries in the world. Therefore, the approach used in this study may not be
representative of global results and further study of this nature should be replicated in other areas. In
addition, this study is limited by its sampling that only the elderly people who could travel outdoor were
interviewed during the data collection. Thus, the result may be different if the elderly who cannot travel
are also interviewed.
9.1.3 Elderly carpool support program by neighborhood drivers in the area with poor accessible
transportation
Because some elderly people lived in the area that public transport services were difficult to access, the
improvement of the level of service of public transport may not be able to address the problem of
feelings of social exclusion-related transportation difficulty of elderly living in those area. Therefore,
the aim of this part was to find solutions for improving the mobility of those Bangkok elderly by
proposing the elderly carpool support program by neighborhood drivers.
The data indicated that 35.92% and 48.54 of Bangkok elderly preferred using this carpool service from
resident living in the same town with them to drive them to their destinations on weekday and weekend,
respectively. By applying the binary logit model to investigate which factors affected the decision to
join the elderly carpool support program, the results indicate that most elderly living in the areas that
were far from public transport station and difficult to call paratransit (e.g. taxi and tuk tuk) wanted the
use this carpool service. For those who experienced the difficulty of daily driving, carpool was also
needed for them. However, some elderly needed the support from others during conducting activity
outside home. These residents need to go out with family or neighbors who can not only get them to
their destination but also take care of them while they are conducting activities. In addition, those
elderly, who felt anxiety of using carpool service from stranger driver that they were not familiar with,
tended not to select using carpool service.
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The results showed that 48.11% and 31.13% of Bangkok residents were motivated to support elderly in
carpool service on their workday (along their commuting route) and holiday (not along their commuting
route), respectively. Most resident were willing to service elderly for free during their commuting trip
because they did not have to pay any extra travel cost. However, those who were motivated to support
elderly on holiday requested supported money from elderly to defray their extra travel cost from
servicing elderly on non-commuting route. Actually, volunteering is unpaid by definition. Nevertheless,
residents willing to support elderly on their holiday can be called ‘paid volunteering’ because they
requested to be paid to for their extra travel costs.
The binary logit model of the decision to support elderly defines that not only elderly but also residents
felt anxiety if they were supposed to service unknow elderly. Those residents who had lower confident
level in driving supporting elderly were not likely to be willing to support elderly. Finally, service price
and free time were also the factors affecting the decision to service elderly on holiday.
During the carpool service on weekend or holiday, KLP analysis was applied to determine an acceptable
range of carpool service prices for services based on the perceptions of the elderly. It was found that
elderly people were not willing to use the service when the price went beyond the maximum price
(32.59 JPY per kilometer) and they had doubted about the quality of the volunteer service when the
service price was lower than the minimum price (16.91 JPY per kilometer). In addition, the binary logit
model of the decision to volunteer of those supply showed the effects of a change in service price on
the number of motivated residents who were willing to support elderly for travel needs. According to a
simulation of the ratio of supply to demand (S/D), the potential supply could not cover the demands of
the elderly at the standard price (24.55 JPY per kilometer), but the S/D ratio would be exactly equal to
1 at the equilibrium price (28.00 JPY per kilometer). Thus, the service should be subsidized by 3.4 JPY
per kilometer to make the elderly feel that the price is not expensive.
Alternatively, based on the factor affecting the decision to support elderly of motivated residents, the
Bangkok government can also encourage them to support elderly by reducing the anxiety level of them
concerning their personal safety when they provide services to unknown elderly. Registration and
database systems can be created to increase the reliability of the volunteer system, resulting in reduced
anxiety levels. In addition, increasing confidence in elderly support skills by holding workshops to train
residents and provide basic knowledge of how to support the elderly through carpool program can
persuade more residents to service elderly.
According to the statistical t-test, the results show that recreational trip frequency increased significantly
by 0.797 times per week if they were supposed to be supported by carpool service, which is
corresponding with the previous research about the provision of door-to-door service of Bangkok
elderly of Srichuae et al. (2016). However, administrative trip frequency did not increase significantly.
The result indicated that the degree of feelings of social exclusion in terms of feeling apart from society
significantly reduced by 0.893 for by having carpool service, but the degree of feeling unable to access
social resource, service and opportunity did not significantly creased due to the analysis. This increased
of trip frequency and decrease of degree of feeling social exclusion were significantly related by 0.91
Pearson correlation.
Policy makers can use these findings regarding pricing and other effects. The methodology in this study
can be used by other areas in Thailand as well as internationally. However, the sample size of the current
study was relatively small because it took a considerable amount of time to collect the data due the long
interviews. Thus, although the analysis of this sample could represent the travel behavior of Bangkok
114
population based on the statistical theory, the results for the supply-demand ratio were limited by their
reliance on only the origin and destination (O-D) of the collected samples. The same procedures can be
replicated with the total O-D data of Bangkok residents that can be obtained using the extended
Bangkok Transport Model (eBUM) (Office of Transport and Traffic Policy and Planning, 2015). In
addition, this study focuses only on those willing to join the system, while other aspects related to this
volunteer service such as calling system, regulation and taxation should be studied more extensively in
the future.
9.2 Policy Implication
Until this point, the problem of social exclusion caused by transport difficulty of low-income and elderly
has been become the current crucial issue that should receive higher attention from the government and
policy maker. To address this problem, this study would recommend the transportation policy based on
the study result which have been rarely focus in the national plan of the Ministry of Transport as follow.
9.2.1 Policy implication from chapter 6 for Bangkok low income
For Bangkok low-income, public transport subsidy should be introduced in order to encourage them to
travel more frequently to participate in more social activity and feel less social exclusion. Based on the
result, the efficiency of increasing trip frequency depended on a different amount of total travel cost of
those low income before and after subsidizing. However, subsidizing every transport routes may be
difficult for the government due the limited budget. The finding shows that non-fixed schedule activities
(e.g. part-time business, visiting friend and relatives, and leisure activities) had more influence on the
feeling of social exclusion than duty activity. Therefore, the mode and route with highest demand for
non-duties activities of those low-income would be the efficient choice to be subsidized.
As mentioned above, the transport subsidy in Thailand is a relatively universal policy that subsidize to
everybody. The result of the study showed that only low income tended to received benefit in terms of
the reduction of social exclusion from the subsidy. Thus, in order to avoid the distribution of subsidy
budget to unintended benefit who are non-low income, this study recommends the subsidy policy
subsidizing for only low-income group (target group) to reach the most efficiency of the distribution of
this subsidy policy to the target group. To remove this unintended beneficial group, the registration of
the specific identification card, given to only low-income persons who evidentially prove their income
level to the official, which must be shown at the ticket booth to get free ticket, was recommended in the
study.
9.2.2 Policy implication from chapter 7 for Bangkok elderly
Based on the result, the improvement of the convenience of transport services is necessary to encourage
elderly to participate in more social activities, resulting in the reduction of feeling social exclusion. The
mode that has most elderly users was bus and the mode that elderly users felts the highest degree of
social exclusion was taxi. It has to be noted that there are many aspects of convenience to be improved,
but the most efficient way found in this study was that the bus service frequency should be increased
during weekdays, when most of the elderly had to rely on their own transportation, especially on the
115
routes that link them to places to shop and engage in non-duty activities. As the number of buses per
hour increases, the probability that the elderly will find a seat will also increase. In addition, the number
of priority seats can be increased without a substantial impact on service performance because when no
elderly passenger is present, other passengers can sit on those seats.
As mentioned, elderly taxi users had the most severe degree of feeling social exclusion which needed
to be addressed. Walking distance was not a problem because taxis offer door-to-door service. However,
the proportional fare (excluding a 35-Baht fixed charge) was expensive for them and thus affected their
degree of satisfaction. Furthermore, some taxi drivers unreasonably denied elderly passengers. The
mechanism was that driver denied picking up elderly passenger when elderly called the taxi. Therefore,
they needed to call many taxis until they found a driver who was willing to service them. The cost per
ride of these kinds of door-to-door services could be brought down by for example subsidy policy, and
the drivers could be specially trained to be ready to support and service elderly people with more
reliability. These approaches can generate the largest effect of reducing degree of social exclusion for
elderly public transport users.
9.2.3 Policy implication from chapter 8 for Bangkok elderly living the area with poor accessible
transportation services
For elderly living in the area where public transport services and any paratransit (e.g. taxi, tuk tuk, Uber
and DRT) are not be easily accessed, the introducing of carpool service by neighborhood driver is
necessary in order to improve the mobility level of those elderly, especially the route for travelling to
participate in leisure activities in the city area of Bangkok. Based on the result, neighborhoods would
like to service elderly for free as volunteers in the same route that they generally commute. For non-
commuting-route service, at the price that amount of supply (nationhood driver) could cover demand
(elderly users), those who were motivated to support elderly on holiday requested supported money
from elderly to defray their extra travel cost from servicing elderly for 28.00 JPY per kilometer, but this
price was higher than the standard price (24.6 JPY per kilometer) that elderly were willing to pay. Thus,
the service should be subsidized by 3.4 JPY per kilometer to make most elderly feel that the price is not
expensive. In addition, this carpool program can be serviced just from the resident area to the public
transport station, and subsequently elderly can use the public transport service which will be improved
in in terms of convenience too, as mentioned before. Nevertheless, it should be noted that the scope of
this study focuses only on those only willing to join the system, while other aspects such as calling
system, law, regulation and taxation should be studied further if the government wants to implement
this policy.
9.3 Research Contribution
This study was particularly useful to evaluate and improve each section of transportation service policy
to address the problem of social exclusion in the future. It will bring widespread benefits to encourage
social inclusion of low-come and elderly groups in the country which has been rarely focused before.
For national and policy appraisal sectors, the policy implication in this study can fulfill low mobility
transport support plan of the Ministry of Transport, as well as can be the efficient approach to combat
social exclusion for low-income and elderly people.
116
In the academic aspect, this study proposes the alternative method to measure the degree of feeling
social exclusion that was not only relying on existing transport ability but also relying on the actual
desired transport ability and personnel feeling (psychological status) of the respondent. Thus, the
methods proposed in this study can be applied for the further research in the future.
9.4 Limitation and Recommendation
Proposed soft-infrastructure policies in the study were picked up as the appropriate transport policies to
address the issue of social exclusion of low-income and elderly groups, based on the current situation
in Bangkok. However, it has to be noted that the problem of social exclusion of those low-mobility and
elderly population cannot be solved by only directive transportation policy, but it rather needs
multidisciplinary strategic policies and collaboration from other sectors, such as health and welfare
policies, to sustainably address this problem. Therefore, the integration and cooperation between
transportation sector and other sectors should be considered in order to meet the goal of promoting
social inclusion for low-mobility group.
The study developed the new aspect of the measurement the degree of social participation by
introducing the concept of gaps in number of trip. In addition, the psychological question to directly
measure the degree of feeling social exclusion was applied in the study. The sample size of the current
study was able to represent the whole Bangkok population based on the statistical theory (comparison
of the proportion of characteristic, such as age and income of sample and population), but sample size
was relatively small because it took a considerable amount of time to collect the data due the long
interviews. Therefore, the result may not represent the actual total O-D of Bangkok population. Thus,
the expansion of the sample size should be considered in the future in order to meet the accuracy of the
travel demand.
117
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APPENDIX A
A) The questionnaire of the Case Study A (English version)
The Questionnaire of the School of Engineering, Asian Institute of Technology
Questionnaire No. (for intercity trip only)
1. Trip purposes:
Travel to visit hometown (go to 2)
Travel to visit friend or family (go to 2)
Travel for education (go to 3)
Travel to work or for business (go to 4)
Travel for leisure or hobby (go to 5)
Travel for health activity (go to 6)
Travel to shopping (go to 7)
Other purpose: please define: __________ (go to 8)
2. If respondents travel to visit hometown or travel to visit friend or family
Question Yes No Note
Respondents travel in higher frequency after using free
train
If No: stop
If Yes: Frequency of trip
before: ____ time/year
after: ____ time/year
Respondent, friend or family moved their resident
location
Respondents had urgent business or emergency that
make them needs to travel in higher frequency
Respondents want to travel in more frequency because
they want to travel by their decision without free train
condition
Other reason that makes respondents travel in more
frequency (write in note)
3. If respondents travel for education
Question Yes No Note
Respondents travel in higher frequency after using free
train
If No: stop
If Yes: Frequency of trip
before: ____ time/year
after: ____ time/year
Respondents have had new study place that makes them
need to travel in more frequency
Respondents was asked to go to study in higher
frequency
Other reason that makes respondents travel in more
frequency (write in note)
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4. If respondents travel to work or for business
Question Yes No Note
Respondents travel in higher frequency after using free
train
If No: stop
If Yes: Frequency of trip
before: ____ time/year
after: ____ time/year
Respondents have had new working place or business
that makes them need to travel in more frequency
Respondents were asked to go to work or do business in
higher frequency
Respondents want to expand their business
Other reason that makes respondents travel in more
frequency (write in note)
5. If respondents travel for leisure or hobby
Question Yes No Note
Respondents travel in higher frequency after using free
train
If No: stop
If Yes: Frequency of trip
before: ____ time/year
after: ____ time/year
Respondents have had new hobby or vocational place
that makes them need to travel in more frequency
Respondents want to travel in more frequency because
they want to travel by their decision without free train
condition
Other reason that makes respondents travel in more
frequency (write in note)
6. If respondents travel for health activity
Question Yes No Note
Respondents travel in higher frequency after using free
train
If No: stop
If Yes: Frequency of trip
before: ____ time/year
after: ____ time/year
Respondents have just received golden card policy (can
access every health service in 30 bath), so they can
travel to receive health service any time they want
Respondents have just received other benefit from
insurance, so they can travel access health service in
more frequency
Respondents have been sick that makes them need to
travel for health service
Doctor made more appointment
Other reason that makes respondents travel in more
frequency (write in note)
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7. If respondents travel to shopping
Question Yes No Note
Respondents travel in higher frequency after using free
train
If No: stop
If Yes: Frequency of trip
before: ____ time/year
after: ____ time/year
There was any reason that make respondents need to
shopping in more frequency
Respondents want to shopping in more frequency
because they want to travel by their decision without
free train condition
Other reason that makes respondents travel in more
frequency (write in note)
8. If respondents travel for other purpose
Question Yes No Note
Respondents travel in higher frequency after using free
train
If No: stop
If Yes: Frequency of trip
before: ____ time/year
after: ____ time/year
Respondents want to travel in more frequency because
they want to travel by their decision without free train
condition
Other reason that makes respondents travel in more
frequency (write in note)
2. Socio-Demographic information
Gender: Male Female
Age: __________ years
Educational level:
Primary school
Secondary school
High school
Vocational Diploma
Bachelor’s degree or higher
Congenital disease or disability: Has Does not have
Married status: Married Single
Number of member in family: ________ person
Resident location: District: __________ Province: __________
3. Economic information
Car owner ship: Yes No (if Yes: Number of car: _____ vehicle) Motorcycle owner ship: Yes No (if Yes: Number of motorcycle: _____ vehicle)
Employment status: Yes No
Occupation: __________
Respondent’s income per month: __________ Baht
Number of member with no income in family: __________ person
Household income: __________ Baht
4. Feeling of social exclusion ( 1:not feel to 5: feel)
Before using free train__________
After using free train__________
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B) The questionnaire of the Case Study A (Thai version)
แบบสอบถามจากสถาบนเทคโนโลยแหงเอเชย (Asian Institute of Technology) คณะวศวกรรมศาสตร แบบสอบถามชด เลขท (Thai version: การเดนทางระหวางเมองเทานน)
1) วตถประสงคในการเดนทาง • เพอกลบภมล าเนา (ไปขอ 2)
• เพอไปเยยมครอบครว หรอ เพอน (ไปขอ 2)
• เพอกจกรรมทางการศกษา (ไปขอ 3)
• เพอท างาน หรอ ธรกจ (ไปขอ 4)
• เพอทองเทยว หรอ งานอดเรก (ไปขอ 5)
• เพอกจกรรมทางสขภาพ (ไปขอ 6)
• เพอชอปปง (ไปขอ 7)
• อนๆ โปรด ระบ: …………………… (ไปขอ 8)
2) เดนทางเพอกลบภมล าเนา และเยยมครอบครวหรอเพอน
ค าถาม ใช ไมใช หมายเหต
ผตอบมความถในการเดนทางในวตถประสงคนเพมขนหลงจากการใชรถไฟฟร
เดม .........ครง ตอป ปจจบน ..........ครง ตอป ถาไมใช หยดท า
ผตอบ หรอ ครอบครว ยายทอย ท าใหเดนทางบอยขน
ผตอบ มธระ หรอ เหตฉกเฉน ท าใหตองเดนทางบอยขน ผตอบ เดนทางบอยขนเนองจากความตองการของเขาเอง ไมเกยวกบรถไฟฟร
มเหตผลอนๆทใหเดนทางบอยขน (โปรดระบในชองหมายเหต)
3) เดนทางเพอกจกรรมทางการศกษา
ค าถาม ใช ไมใช หมายเหต
ผตอบมความถในการเดนทางในวตถประสงคนเพมขนหลงจากการใชรถไฟฟร
เดม .........ครง ตอป ปจจบน ..........ครง ตอป ถาไมใช หยดท า
ผตอบ ยายสถานทศกษา ท าใหเดนทางบอยขน ผตอบ ไดรบมอบหมายใหไปท ากจกรรมการศกษานอกสถานทบอยขน ท าใหเดนทางบอยขน
มเหตผลอนๆทใหเดนทางบอยขน (โปรดระบในชองหมายเหต)
131
4) เดนทางเพอท างานหรอท าธรกจ ค าถาม ใช ไมใช หมายเหต
ผตอบมความถในการเดนทางในวตถประสงคนเพมขนหลงจากการใชรถไฟฟร
เดม .........ครง ตอป ปจจบน ..........ครง ตอป ถาไมใช หยดท า
ผตอบ ยายสถานทท างาน หรอ ท าธรกจ ท าใหเดนทาง บอยขน
ผตอบ ไดรบมอบหมายใหไปท างานบอยขนหรอมธรกจเพม ท าใหเดนทางบอยขน
ผตอบ ตองการขยายธรกจเพม ท าใหเดนทางบอยขน มเหตผลอนๆทใหเดนทางบอยขน
(โปรดระบในชองหมายเหต)
5) เดนทางเพอทองเทยว หรอ งานอดเรก
ค าถาม ใช ไมใช หมายเหต
ผตอบ มความถในการเดนทางในวตถประสงคนเพมขนหลงจากการใชรถไฟฟร
เดม .........ครง ตอป ปจจบน ..........ครง ตอป ถาไมใช หยดท า
ผตอบ มงานอดเรก หรอ สถานททองเทยวใหม ท าใหเดนทางบอยขน ผตอบ ตองการท างานอดเรก หรอ ทองเทยวเพม เนองจากความตองการของเขาเอง ไมเกยวกบรถไฟฟร
มเหตผลอนๆทใหเดนทางบอยขน (โปรดระบในชองหมายเหต)
6) เดนทางเพอกจกรรมทางสขภาพ
ค าถาม ใช ไมใช หมายเหต
ผตอบ มความถในการเดนทางในวตถประสงคนเพมขนหลงจากการใชรถไฟฟร
เดม .........ครง ตอป ปจจบน ..........ครง ตอป ถาไมใช หยดท า
ผตอบ ไดรบนโยบาย "30 รกษาทกโรค "ท าใหเดนทางไปพบแพทยไดบอยขน
ผตอบ มประกนสขภาพ ท าใหเดนทางไปพบแพทยไดบอยขน
ผตอบ เพงเจบปวย หลงการใชรถไฟฟร
ผตอบ เดนทางไปพบแพทยบอยขน เพราะแพทยนดบอยขน มเหตผลอนๆทใหเดนทางบอยขน (โปรดระบในชองหมายเหต)
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7) เดนทางเพอชอบปง ขอ ค าถาม ใช ไมใช หมายเหต
7.1 ผตอบ มความถในการเดนทางในวตถประสงคนเพมขนหลงจากการใชรถไฟฟร
เดม .........ครง ตอป ปจจบน ..........ครง ตอป ถาไมใช หยดท า
7.2 มเหตไดๆ ทท าใหตองซอสนคาบอยขน ท าใหเดนทางบอยขน
7.3 ผตอบ ตองการชอบปงบอยขน เนองจากความตองการของเขาเอง ไมเกยวกบรถไฟฟร
7.4 มเหตผลอนๆทใหเดนทางบอยขน (โปรดระบในชองหมายเหต) 8) เดนทางเพอวตถประสงคอนๆ
ขอ ค าถาม ใช ไมใช หมายเหต
8.1 ผตอบ มความถในการเดนทางในวตถประสงคนเพมขนหลงจากการใชรถไฟฟร
เดม .........ครง ตอป ปจจบน ..........ครง ตอป ถาไมใช หยดท า
8.5 ผตอบ ตองการเดนทางบอยขน เนองจากความตองการของเขาเอง ไมเกยวกบรถไฟฟร
8.6 มเหตผลอนๆทใหเดนทางบอยขน (โปรดระบในชองหมายเหต)
2) ขอมลทางสงคม
2.1) เพศ : ชาย หญง 2.2) อาย : ……………ป 2.3) ระดบการศกษา:
1. ประถมศกษา 4. ปวส หรอ อนปรญญา 2. มธยมศกษาตอนตน 5. ปรญญาตร หรอ สงกวา 3. มธยมศกษาตอนปลาย
2.4) โรคประจ าตว หรอ ความพการ : ไมม ม (ถาม): โปรดระบ………………………..
2.5) สถานะ การสมรส : สมรส โสด 2.6) จ านวนสมาชกในครอบครว: ……………………คน 2.7) ทอยปจจบน: อ าเภอ/เขต…….……………..……… จงหวด………………………….…… 3) ขอมลทางเศรษฐศาสตร
3.1) ทานมรถยนตหรอไม: ม ไมม (ถาม): โปรดระบจ านวน………...คน 3.2) ทานมรถจกรยานยนตหรอไม : ม ไมม (ถาม): โปรดระบจ านวน………...คน 3.3) สถานะ การท างาน : ท างาน ไมไดท างาน
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3.4) (ถาท างาน) : โปรดระบอาชพ………………….……… 3.5) รายไดตอเดอนของผตอบแบบสอบถาม : ………….………บาท 3.6) จ านวนผทไมไดท างานหรอไมมรายไดในครอบครว : ………...……….คน 3.7) รายไดรวมทงครอบครว ตอ เดอน : ………….………บาท
4) ความรสกไมเปนสวนหนงของสงคม (1 ไมรสก ถง 5 รสก) 4.1) กอนใชรถไฟฟร………….……… 4.2) หลงใชรถไฟฟร………….………
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C) The survey pictures of the Case Study A
Data collection at Hua Lamplong station
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APPENDIX B-1
A) The questionnaire of the Case Study B-1 (English version)
The study of travel behaviour of elderly people
The researchers of the Faculty of engineering, Chulalongkorn University cooperated with
Hokkaido University, Japan are conducting the research on travel behaviour of older people. The
study aims to examine the degree of satisfaction with transportation. We would like to hear your
answer and opinions. The interview should only take 10 to 15 minutes, and your responses are
completely anonymous.
Instruction for Interviewer
I. Section 1: Interviewer asks respondent about socio-demographic.
II. Section 2: Interviewer asks respondent about transport information and satisfaction level.
Table 1: Transportation mode (Q.2.1)
Table 2: Likert scale
Explanation for Satisfaction level with Transportation (Q.4.1-4.8)
I. Travel cost refers to how expensive or reasonable of travel cost.
II. Walkway condition refers to weather, pollution, separation from traffic, width, crowded and safety.
III. Service frequency refers to number of trips per day, and service span (for fixed route service).
IV. Service information refers to being able to understand the service information, map, ticketing, schedule, and announcement.
V. Service safety refers to personal security or felling safe of misbehavior of staff or driver, theft, attack, harassment on vehicle,
and safety of operation, infrastructure and any equipment of vehicle.
VI. Service reliability refers to reliability, punctuality, not denied or missed trips, and ability to complain of the poor service.
VII. Service comfort and convenience refers to appearance and comfort of passenger amenities such as waiting area, passenger
assistance, step access to get on and off, adequate seat, crowded on vehicle, cleanliness and toilet.
VIII. Traffic condition refers to the amount of traffic on the road.
Code Transportation mode Code Transportation mode
0 Some one sending you 7 Boat
1 Walking 8 MRT or BTS
2 Cycling 9 Train
3 Private car 10 Taxi
4 Private motorcycle 11 Tuk Tuk
5 Bus 12 Motor cycle service
6 Van 13 Relying on others
Likert Scale A: Level of Quality Scale B: Level of Agreement Scale C: Level of Satisfaction
1 Unavilable Disagree Strongly Completely dissatisfied
2 Very Poor Disagree Moderately Mostly dissatisfied
3 Poor Disagree Slightly Somewhat dissatisfied
4 Average Average Neither satisfied or dissatisfied
5 Good Agree Slightly Somewhat satisfied
6 Very Good Agree Moderately Mostly satisfied
7 Excellent Agree Strongly Completely satisfied
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Interview Step for section 2, 3 and 4
1. Question 2.1 to 3.1, interviewer asks respondent about current situation and their expectation in the future (only the case of better transport condition: the higher frequency
is not cause by other factors such as higher income or time)
2. If respondent has many activities for each kind, interviewer asks only the most frequent activity and ask only major mode
3. If respondent use the same transportation mode, interviewer interviews only 1 time per each mode
4. For topic 3 and 4, if transportation mode is:
0 (ask only 3.1, 3.2, 4.5, 4.7 and 4.10) 1 or 2 (ask only 3.1, 3.2, 4.2, 4.5, 4.7, 4.9 and 4.10)
3 or 4 (ask only 3.1, 3.2, 3.3, 4.1, 4.5, 4.7, 4.8 and 4.10) 4 or 5 (do not have to ask 3.6)
7, 8 or 9 (do not have to ask 3.6 and 4.8) 10, 11 or 12 (do not have to ask 3.5, 3.7, 3.8 and 4.3)
Current Expectation Current Expectation Current Expectation Current Expectation
2.1 Transportation mode (using Table A)
2.2 Total frequency of activities (time/week)
2.3 Destination (name)
3.1 Travel distance (kilometer)
3.2 Travel time (minute)
3.3 Travel cost (Baht)
3.4 Access and egress walking distance (meter)
3.5 Service frequency (time/hour)
3.6 How often dose the driver deny the passenger (%, For DRT)
3.7 Crowded on vehicle (1 to 7, using Scale B)
3.8 Seat availability (1 to 7, using Scale B)
4.1 Satisfaction level of travel cost
4.2 Satisfaction level of walkway condition and environment
4.3 Satisfaction level of service frequency
4.4 Satisfaction level of ability to know the service information
4.5 Satisfaction level of service safety
4.6 Satisfaction level of service reliability and punctuality
4.7 Satisfaction level of service comfort and convenience
4.8 Satisfaction level of road traffic condition
4.9 Sastisfaction level of parking
4.10 Overall satisfaction level of each transportation
2.Trips Information
3.Transport Performance
Section 2: please answer the questions bellow
(□ is important level; to 7, in the blanket is time)
Mandatory activities
Shopping □ (_____) Documentary activities □ (_____) Financial activities □ (_____) Heath care or hospital □ (_____)
4.Sastisfaction with Transportation (1 to 7, using Scale C)
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Current Expectation Current Expectation Current Expectation Current Expectation
2.1 Transportation mode (using Table A)
2.2 Total frequency of activities (time/week)
2.3 Destination (name)
3.1 Travel distance (kilometer)
3.2 Travel time (minute)
3.3 Travel cost (Baht)
3.4 Access and egress walking distance (meter)
3.5 Service frequency (time/hour)
3.6 How often dose the driver deny the passenger (%, For DRT)
3.7 Crowded on vehicle (1 to 7, using Scale B)
3.8 Seat availability (1 to 7, using Scale B)
4.1 Satisfaction level of travel cost
4.2 Satisfaction level of walkway condition and environment
4.3 Satisfaction level of service frequency
4.4 Satisfaction level of ability to know the service information
4.5 Satisfaction level of service safety
4.6 Satisfaction level of service reliability and punctuality
4.7 Satisfaction level of service comfort and convenience
4.8 Satisfaction level of road traffic condition
4.9 Sastisfaction level of parking
4.10 Overall satisfaction level of each transportation
Leisure activities 1 □ (____):_______*
2.Trips Information
Non-mandatory activities
Leisure activities 2 □ (____):_______*
3.Transport Performance
Section 2: please answer the questions bellow
(□ is important level; to 7, in the blanket is time) Meeting relative outside home □ (____) Meeting friend outside home □ (____)
4.Sastisfaction with Transportation (1 to 7, using Scale C)
5.1 You feel alone or being isolated from society
5.2 You have adequate links with relative living tin other houses
5.3 You have adequate links with friends
5.4 You are able to participate social activities
5.5 You are able to access social resource
5.6 You satisfy with being part of the society
5.7 You satisfy with quility of life
5.Satisfaction with Life (1 to 7, using Scale B) RecentlyIf you are able to participate activities
as often as you like
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B) The questionnaire of the Case Study B-1 (Thai version)
การศกษาพฤตกรรมการเดนทางของผสงวย
ทางคณะวศวกรรมศาสตร จฬาลงกรณมหาวทยาลย รวมกบ มหาวทยาลยฮอกไกโด ประเทศญป น ไดท าการศกษาเกยวกบพฤตกรรมการเดนทางของผสงวย โดยมวตถประสงคเพอตองการทดสอบระดบความพงพอใจของผสงวย จงเรยนขอความอนเคราะหจากทานในการใหขอมล ซงการสมภาษณจะใชเวลาด าเนนการประมาณ 10 ถง 15 นาท โดยทางผศกษาจะไมท าการถามชอและนามสกลของทาน
และ ขอมลทกอยางจะถกปดเปนความลบ
ค ำแนะน ำส ำหรบผสมภำษณ
III. สวนท 1 ผสมภาษณสอบถามขอมลทางดานเศรษฐกจและสงคม
IV. สวนท 2 ผสมภาษณสอบถามขอมลเกยวกบการเดนทางและระดบความพงพอใจ
ตารางท 1 ประเภทของการเดนทาง ( ขอ 2.1)
ตารางท 2 ลเครต สเกล
ค ำอธบำยส ำหรบกำรถำมระดบควำมพงพอใจเกยวกบระบบขนสง( ขอ 4.1 ถง)4.8
IX. ราคาเดนทาง หมายถงความเหมาะสมของราคาเดนทาง
X. สภาพแวดลอมและสงแวดลอมของทางเดน หมายถง สภาพอากาศ มลพษ การแบงแยกทาวเดนออกจากถนน ความกวางของทางเดน ความแออดบนทางเดนเชนจากรานแผงลอย และ ความปลอดภย
XI. ความถในการใหบรการ หมายถงจ านวนเทยวตอวน และ ระยะเวลาในการใหบรการ ( ส าหรบการบรการแบบก าหนดเสนทางตายตว )
XII. ความสามารถในการรบรขอมลตางๆของระบบขนสง หมายถงความสามารถในการรบรขอมล แผนท การซอและจองตวโดยสาร ตารางเวลาในการใหบรการ และ การประกาศตางๆ
XIII. ความปลอดภยในการเดนทาง หมายถงการรกษาความปลอดภย ความรสกปลอดภยจากพฤตกรรมของพนกงานและผขบขยานพาหนะ ผไมประสงคด และรวมไปถงความปลอดภยของการใหบรการ โครงสราง และอปกรณตางๆของยานพาหนะ ,หากเปนการขบขยานพาหนะสวนบคคล การเดน และ
หมายถงความปลอดภยบนทองถนน และ ทางเดนการปนจกรยาน ให
XIV. ความนาเชอถอและความตรงตอเวลา หมายถงความนาเชอถอในการใหบรการ ความตรงตอเวลา การรกษาสญญา การไมปฏเสธผโดยสาร และ ความสามารถในการแนะน าการใหรการหากไมพอใจในการใชบรการ
XV. ความสะดวกสบายในการเดนทาง หากเปนระบบขนสงสาธารณะใหหมายถง ภาพลกษณ ความสะดวกสบาย สงอ านวนความสะดวกตางๆเชน สถานทรอรถ พนกงานผชวยเหลอผโดยสาร การขนและลงจากยานพาหนะ ความเพยงพอของทนง ความแออดบนยานพาหนะ ความสะอาด และ หองน า หากเปนพาหนะสวนบคคลใหหมายถงความสะดวกสบายในการขบขยานพาหนะ
XVI. สภาพการจราจรบนถนน หมายถงปรมาณยานพาหนะบนทองถนน และ ความแออดบนทองถนน
รหส ประเภทการเดนทาง รหส ประเภทการเดนทาง
0 มคนไปสง 7 เรอ
1 เดนเทา 8 รถไฟฟา
2 จกรยาน 9 รถไฟ
3 ยานพาหนะสวนบคคล 10 แทกซ
4 จกรยานยนตสวนบคคล 11 ตกตก
5 รถเมล 12 จกรยานยนตรบจาง
6 รถต 13 พงพาอาศยผอ น
ลเครต สเกล สเกล ก. ระดบคณภาพ สเกล ข. ระดบการเหนดวย สเกล ค. ระดบความพงพอใจ
1 แยมากทสด ไมเหนดวยมากทสด ไมพอใจอยางยง
2 แย ไมเหนดวย ไมพอใจ
3 แยเลกนอย ไมเหนดวยเลกนอย ไมพอใจเลกนอย
4 ปานกลาง ปานกลาง เฉยๆ
5 ดเลกนอย เหนดวยเลกนอย พอใจเลกนอย
6 ด เหนดวย พอใจ
7 ดเยยม เหนดวยมากทสด พอใจอยางยง
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141
ณ ปจจบน ความตองการ ณ ปจจบน ความตองการ ณ ปจจบน ความตองการ ณ ปจจบน ความตองการ
2.1 ประเภทการเดนทางหลก (ตาราง ก)
2.2 ความถทงหมด(รวมทกกจกรรมยอย) ในการเดนทางเพอไปท ากจกรรม (ครง/สปดาห)
2.3 จดหมายปลายทาง (ชอ)
3.1 ระยะทางในการเดนทาง (กโลเมตร)
3.2 ระยะเวลาในการเดนทาง (นาท)
3.3 ราคาในการเดนทาง (บาท)
3.4 ระยะการเดนเพอเขาถงสถานและเพอไปถงจดหมายปลายทาง (เมตร)
3.5 ความถในการใหบรการของระบบขนสง (ครง/วน)
3.6 ความถทผขบขปฏเสธผโดยสาร (สดสวน, ส าหรบแทกซ รถตกตก และ วนมอรเตอไซด)
3.7 ความแออดบนยานพาหนะ (ใหคะแนน 1 ถง 7 ตามเสกล ข.)
3.8 ทานสามารถไดทนงไดบนยานพาหนะ (ใหคะแนน 1 ถง 7 ตามเสกล ข.)
4.1 ความพงพอใจใน ราคาเดนทาง
4.2 ความพงพอใจใน สภาพแวดลอมและสงแวดลอมของทางเดน
4.3 ความพงพอใจใน ความถในการใหบรการของระบบขนสงน
4.4 ความพงพอใจใน ความสามารถในการรบรขอมลตางๆของระบบขนสง
4.5 ความพงพอใจใน ความปลอดภยในการเดนทาง
4.6 ความพงพอใจใน ความนาเชอถอและความตรงตอเวลา
4.7 ความพงพอใจใน ความสะดวกสบายในการเดนทาง
4.8 ความพงพอใจใน สภาพการจราจรบนถนน
4.9 ความพงพอใจใน การจอดรถ
4.10 ความพงพอใจใน ภาพรวมของระบบขนสงน
สวนท 2: โปรดตอบค าถามดงตอไปน (□ ระดบความส าคญ 1-7, เวลา)
2.ขอมลการเดนทาง
3.สภาพการเดนทางในปจจบน
4.ระดบความพงพอใจเกยวกบการเดนทาง (ใหคะแนน 1 ถง 7 ตามเสกล ค.)
กจกรรมทจ าเปนตองท า
ชอปปง □ (________) ธระดานเอกสาร □ (________) คลนกหรอโรงพยาบาล □ (________)ธรกรรมทางการเงน □ (________)
2 3 4
1. ตงแตค าถามท 2.1 ถง 3.1 ใหผสมภาษณถามถงความถและสถานทในการท ากจกรรมในสถานการณจรง กอน จากนนถามถงความถและสถานททผตอบม ท ากจกรรม
( )
2. ในหนงหวขอกจกรรมนนๆ หากผตอบมหลายกจกรรมยอยๆ เชน ในหวขอกจกรรมยามวาง ผตอบทงเลนกฬาและทงท างานอดเรก ใหผสมภาษณสมภาษณเฉพาะ และถา
หากผตอบใชบรการหลายประเภทการเดนทางใหสมภาษณเฉพาะ
3.
4. 3 4
0: 3.1 3.2 4.5 4.7 4.10 1 2: 3.1 3.2 4.2 4.5 4.7 4.9 4.10 3 4: 3.1 3.2 3.3 4.1 4.5 4.7 4.8 4.10
5 6: 3.6 7 8 9 4: 3.6 4.8 10 11 12: 3.5 3.7 3.8 4.3
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ณ ปจจบน ความตองการ ณ ปจจบน ความตองการ ณ ปจจบน ความตองการ ณ ปจจบน ความตองการ
2.1 ประเภทการเดนทางหลก (ตาราง ก)
2.2 ความถทงหมด(รวมทกกจกรรมยอย) ในการเดนทางเพอไปท ากจกรรม (ครง/สปดาห)
2.3 จดหมายปลายทาง (ชอ)
3.1 ระยะทางในการเดนทาง (กโลเมตร)
3.2 ระยะเวลาในการเดนทาง (นาท)
3.3 ราคาในการเดนทาง (บาท)
3.4 ระยะการเดนเพอเขาถงสถานและเพอไปถงจดหมายปลายทาง (เมตร)
3.5 ความถในการใหบรการของระบบขนสง (ครง/วน)
3.6 ความถทผขบขปฏเสธผโดยสาร (สดสวน, ส าหรบแทกซ รถตกตก และ วนมอรเตอไซด)
3.7 ความแออดบนยานพาหนะ (ใหคะแนน 1 ถง 7 ตามเสกล ข.)
3.8 ทานสามารถไดทนงไดบนยานพาหนะ (ใหคะแนน 1 ถง 7 ตามเสกล ข.)
4.1 ความพงพอใจใน ราคาเดนทาง
4.2 ความพงพอใจใน สภาพแวดลอมและสงแวดลอมของทางเดน
4.3 ความพงพอใจใน ความถในการใหบรการของระบบขนสงน
4.4 ความพงพอใจใน ความสามารถในการรบรขอมลตางๆของระบบขนสง
4.5 ความพงพอใจใน ความปลอดภยในการเดนทาง
4.6 ความพงพอใจใน ความนาเชอถอและความตรงตอเวลา
4.7 ความพงพอใจใน ความสะดวกสบายในการเดนทาง
4.8 ความพงพอใจใน สภาพการจราจรบนถนน
4.9 ความพงพอใจใน การจอดรถ
4.10 ความพงพอใจใน ภาพรวมของระบบขนสงน
4.ระดบความพงพอใจเกยวกบการเดนทาง (ใหคะแนน 1 ถง 7 ตามเสกล ค.)
สวนท 2: โปรดตอบค าถามดงตอไปน (□ ระดบความส าคญ 1-7, เวลา)
2.ขอมลการเดนทาง
3.สภาพการเดนทางในปจจบน
พบญาตตางบาน □ (________) พบเพอนฝง □ (________) กจกรรมยามวาง1 □ (____) :_____*
กจกรรมทอาจไมจ าเปนตองท า
กจกรรมยามวาง2 □ (____) :_____*
5.1 ทานมความรสกเหงาหรอโดดเดยวจากสงคม
5.2 ทานมความเชอมโยงกบครอบครวตางบาน
5.3 ทานมความเชอมโยงเพอนฝง
5.4 ทานสามารถเขารวมกจกรรมตางๆในสงคมไดอยางเพยงพอ
5.5 ทานสามารถเขาถงทรพยากรในสงคมไดอยางเพยงพอ
5.6 ทานพอใจในการเปนสวนหนงของสงคม
5.7 ทานพอใจในคณภาพชวตของทาน
หากทานสามารถไปท ากจกรรมได
ตามความถท ทานตองการณ ปจจบน5.ระดบความพงพอใจในชวต (ใหคะแนน 1 ถง 7 ตามเสกล ข.)
143
C) The survey pictures of the Case Study B-1
144
APPENDIX B-2
A) The questionnaire of the Case Study B-2 (English version)
The study of volunteering to support elderly’s transportation (Part A)
The questionnaire is a part of the research of the Faculty of engineering, Hokkaido University, Asian
Institute of Technology and Chulalongkorn University. The study aims to investigate the potential of
the volunteering to support elderly for travel needs. We would like to hear your opinions. The interview
would only take 10 minutes, and your responses are completely anonymous.
1 SOCIO DEMOGRAPHIC
1.1 Gender �Male �Female
1.2 Age ____________Year
1.3 Family size ____________Person
1.4 Income ____________Baht/month
2 EXISTING SITUATION
2.1 Home address ______________________
2.1.1 Category of the address �Urban �Suburban
The use of private car
2.2 Can you drive conveniently (1:Inconvineint to 5:Convineint) �(1) �(2) �(3) �(4) �(5)
The use of taxi
2.3 Is taxi service available in your living area �Yes �No
2.3.1 If yes, How difficult do you call athe taxi (1:Difficult to 5:Easy) �(1) �(2) �(3) �(4) �(5)
2.3.2 Reliability of driver �(1) �(2) �(3) �(4) �(5)
The use of public transport
2.4 In your living area, can people travel by using public transport �Yes �No
2.4.1 Distance from your house to nearest public transport station ____________Meter
2.4.2 Ability to gain the information (1:Poor to 5:Good) �(1) �(2) �(3) �(4) �(5)
2.4.3 Convineint level (1:Inconvineint to 5:Convineint) �(1) �(2) �(3) �(4) �(5)
2.5 Your major transportation Mode �Walking or cycling(1)
�Driving(2)
�Bus or van(3)
�Metro(4)
�Taxi(5)
�Someone sending you(6)
2.6 What kind of your health condition makes transportation difficult �Walking ability(1) �Vision(2)
�Hearing ability(3) �No(4)
�Other ________
Your actual trip information
2.9 Shopping, VFR (Visiting friends or relatives), hobby ____________Time/week
2.9.1 Major destination ____________
2.9.2 Travel distance ____________Kilometer
2.9.3 Category of destination �City center(1) �Suburban(2) �Neighborhood(3)
2.9.4 Do you need supporter while you conduct the activity(1:No to 5:Yes) �(1) �(2) �(3) �(4) �(5)
2.10 Administrative (All kinds) ____________Time/week
2.10.1 Major destination ____________
2.10.2 Travel distance ____________Kilometer
2.10.3 Category of destination �City center(1) �Suburban(2) �Neighborhood(3)
2.10.4 Do you need supporter while you conduct the activity(1:No to 5:Yes) �(1) �(2) �(3) �(4) �(5)
2.11 Your degree of feeling social inclusion
2.11.1 You feel part of society (1:No to 5:Yes) �(1) �(2) �(3) �(4) �(5)
2.11.2 You feel able to access social resource (1:No to 5:Yes) �(1) �(2) �(3) �(4) �(5)
2.12 Anxiety level to stranger (1:Non anxiety to 5: Anxiety) �(1) �(2) �(3) �(4) �(5)
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3 WILLINGNESS TO USE THE SERVICE FROM VOLUNTEER SUPPORTER
3.1 What kind of service are you interested in �Door to door service(1)
□Ride sharing(1.1)
□DRT(1.2)
�Public transport supporter(2)
□Training support few times(2.1)
□Training support everytime(2.2)
□Safey support(2.3)
□Others(2.4)_______________
�Not interested(3)
3.1.1 If not interested, others kind of service do you want to get �Improve taxi service(1)
□Expand service area(1.1)
□Reliability of driver(1.2)
□Fare discount(1.3)
□Others(1.4)_______________
�Improve the LOS public transport(2)
□Information system(2.1)
□Safey(2.2)
□Comfotable in vehicle(2.3)
□Fare discount(2.4)
□Punctuality(2.5)
□Others(2.6)_______________
�Others(3)____________________
�No(4)
3.1.2 If the anser of both 3.1 and 3.1.1 is no, why you do not select any option �Anxiety of safety if stranger is your supporter(1)
�No activity supporter at the destination(2)
�Others(3)____________________
Your desired trip information
3.2 Shopping, VFR (Visiting friends or relatives), hobby ____________Time/week
3.2.1 Major destination (If destination is changed) ____________
3.2.2 Travel distance ____________Kilometer
3.2.3 Category of destination �City center(1) �Suburban(2) �Neighborhood(3)
3.3 Administrative (All kinds) ____________Time/week
3.3.1 Major destination (If destination is changed) ____________
3.3.2 Travel distance ____________Kilometer
3.3.3 Category of destination �City center(1) �Suburban(2) �Neighborhood(3)
3.4 Your degree of feeling social inclusion
3.4.1 You feel part of society (1:No to 5:Yes) �(1) �(2) �(3) �(4) �(5)
3.4.2 You feel able to access social resource (1:No to 5:Yes) �(1) �(2) �(3) �(4) �(5)
3.5 What day you prefer to use the service �Weekday �Weekend
3.6 Please fill out the 4 suitable service prices in your opinion -If "ride sharing", fare cost
-If "public transport assistance", personal cost
3.6.1 Reasonable ____________Baht/times
3.6.2 Expensive ____________Baht/times
3.6.3 Too expensive to be willing to use ____________Baht/times
3.6.4 Too cheap to be willing to use ____________Baht/times
**Too cheap refers to the price that you do not use the service because you feel low
quality of service and high risk of safety: e.g. Driving safety
146
The study of volunteering to support elderly’s transportation (Part B)
The questionnaire is a part of the research of the Faculty of engineering, Hokkaido University, Asian
Institute of Technology and Chulalongkorn University. The study aims to investigate the potential of
the volunteering to support elderly for travel needs. We would like to hear your opinions. The interview
would only take 10 minutes, and your responses are completely anonymous.
1 SOCIO DEMOGRAPHIC
1.1 Gender �Male �Female
1.2 Age ____________Year
1.3 Educational level �Junior high school(1) �High school(2)
�Vocational certificate(3) �Vocational diploma(4)
�Bachelor(5) �Master(6)
�Others (________)
1.4 Occupation �Do not work(1) �Student(2)
�Government officer(3) �Private officer(4)
�Private business(5) �Freelancer(6)
�Others (________)
1.5 Income ____________Baht/month
1.6 Car and driving license owner ship �Yes �No
2 EXISTING TRAVEL BEHAVIOR
2.1 Commuting �Weekday �Weekend
2.1.1 Origin _________________
2.1.2 Destination1 _________________
2.1.3 Destination2 (If have) _________________
2.1.4 Transportation Mode �Walking or cycling(1)
�Driving(2)
�Bus or van(3)
�Metro(4)
�Taxi(5)
2.2 Non commuting trip �Weekday �Weekend
2.2.1 Origin _________________
2.2.2 Destination1 _________________
2.2.3 Destination2 (If have) _________________
2.2.4 Transportation Mode �Walking or cycling(1)
�Driving(2)
�Bus or van(3)
�Metro(4)
�Taxi(5)
2.3.1 Free time per day (Weekday) ____________Hour/day
2.3.2 Free time per day (Weekend) ____________Hour/day
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3 WILLINGNESS TO ACCEPT TO SUPPORT ELDERLY
3.1 Recently, do you agree with this concept of supporting elderly �Agree(1) �Disagree(2)
3.2 Do you want to support elderly for ridesharing driver �Yes(1) �No(2)
3.2.1 If no, which reason �You do not like this kind of task(1)
�You are not confident with my driving skill(2)
�No time to support(3)
�You think it is the task of the government(4)
�Others(5)____________________
3.3 Do you want to support elderly for public transport supporter �Yes(1)
□Training support few times(1.1)
□Safety support(1.2)
�No(2)
3.3.1 If no, which reason �You do not like this kind of task(1)
�You are not confident with my support skill(2)
�No time to support(3)
�You think it is the task of the government(4)
�Others(5)____________________
3.4 Available trip of time that you are willing to support elderly �Commuting trip(1)
Why you select1 □It is the same way you go(1.1)
□Comfortable to serve familiar elderly along the way(1.2)
□Others(1.3)_______________
�Non commuting trip(2)
Why you select2 □It is the same way you go(2.1)
□Others(2.2)_______________
�Free time, not the same way you go(3)
Why you select3 □Others(3.1)_______________
If you choose 1 or 2 in the question 3.4
3.5.1 If elderly go the same way with yours, please fill the preferred wage rate (fill 0 if you want to do it for free)
If you choose 1 or 2 in the question 3.4 ____________Baht/kilometer
If you choose "public transport supporter" ____________Baht/time
If you choose 3 in the question 3.4
3.5.2 If elderly do not go the same way with yours, please fill the preferred wage rate (fill 0 if you want to do it for free)
If you choose "ride sharing" ____________Baht/kilometer
If you choose "public transport supporter" ____________Baht/time
3.6 Anything you worry about this task
�Your own safety to serve unknown elderly(1) �(1) �(2) �(3) �(4) �(5)
�Familiarity with elderly you support(2) �(1) �(2) �(3) �(4) �(5)
�Others(3)____________________
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B) The questionnaire of the Case Study B-2 (Thai version)
งานศกษาโครงการอาสาสมครผชวยผสงอายในการเดนทาง (สวน ก)
แบบสอบถามนเปนสวนหนงของงานวจยของคณะวศกรรมศาสตร มหาวทยาลยฮอกไกโด สถาบนเทคโนโลยแหงเอเชย และ จฬาลงกรณมหาวทยาลย ซงงานศกษานมความประสงคทจะศกษาความเปนไปไดในการด าเนนงานโครงการ อาสาสมครผชวยผสงอายในการเดนทาง ทางผวจยมความยนดทจะรบฟงความคดเหนจากทาน โดยการสมภาษณนนจะใชเวลาประมาณ 10 นาท โดยขอมลทกอยางนนจะถกปดเปนความลบ
1 ขอมลทางเศรษฐกจและสงคม
1.1 เพศ �ชาย �หญง
1.2 อาย ____________ ป
1.3 จ านวนสมาชกครอบครว ____________ คน
1.4 รายได ____________ บาท/เดอน
2 ขอมลการเดนทางในปจจบน
2.1 ทอยของทาน ______________________
2.1.1 ประเภทของพนททบานของทานอาศยอย �เขตเมอง �นอกเขตเมอง
การขบขรถยนต
2.2 ทานสามารถขบรถไดอยางสะดวกหรอไม (1=ไมสะดวก ถง 5=สะดวก) �(1) �(2) �(3) �(4) �(5)
การใชบรการแทกซ
2.3 มบรการแทกซในละแวกบานของทานหรอไม �ใช �ไมใช
2.3.1 ถาใช: ระบความยากงายในการเรยกแทกซของทาน(1=ยาก ถง 5=งาย) �(1) �(2) �(3) �(4) �(5)
2.3.2 ความนาเชอถอของคนขบ (1=ไมนาเชอถอ ถง 5=นาเชอถอ) �(1) �(2) �(3) �(4) �(5)
การใชบรการระบบขนสงสาธารณะ
2.4 ผคนในละแวกบานของทานสามารถเดนทางโดยระบบขนสงสาธารณะหรอไม �ใช �ไมได
2.4.1 ระยะทางจากบานของทานไปถงสถานระบบขนสงสาธารณะทใกลทสด ____________ เมตร
2.4.2 การรบรขอมลการเดนทาง (1=ยาก ถง 5=งาย) �(1) �(2) �(3) �(4) �(5)
2.4.3 ความสะดวกสบาย (1=ไมสะดวก ถง 5=สะดวก) �(1) �(2) �(3) �(4) �(5)
2.5 รปแบบการเดนทางหลกททานเดนทางเปนประจ า �เดน หรอ จกรยาน(1)
�ขบรถยนต(2)
�รถเมล หรอ รถต(3)
�รถไฟฟา(4)
�แทกซ(5)
�มคนไปรบไปสง(6)
2.6 สถาณะทางสขภาพของทานทมผลตอความสะดวกสบายในการเดนทาง �การเดน(1) �การมองเหน(2)
�การไดยน(3) �ไมม(4)
�อนๆ ________
การเดนทางในปจจบน
2.9 ชอปปง, พบปะเพอนและญาตตางบาน, กจกรรมยามวางทกประเภท ____________ ครง/สปดาห
2.9.1 จดหมายปลายทางหลก ____________
2.9.2 ระยะทาง ____________ กโลเมตร
2.9.3 รปแบบของจดหมายปลายทาง �ใจกลางเมอง(1) �ชานเมอง(2) �แถวบาน(3)
2.9.4 ทานตองการผชวยเหลอในการท ากจกรรมหรอไม (1:ไม ถง 5:ใช) �(1) �(2) �(3) �(4) �(5)
2.10 กจกรรมทเกยวกบหนาทการจดการทกประเภท (เชนการเงนหรอพบแพทย) ____________ ครง/สปดาห
2.10.1 จดหมายปลายทางหลก ____________
2.10.2 ระยะทาง ____________ กโลเมตร
2.10.3 รปแบบของจดหมายปลายทาง �ใจกลางเมอง(1) �ชานเมอง(2) �แถวบาน(3)
2.10.4 ทานตองการผชวยเหลอในการท ากจกรรมหรอไม (1:ไม ถง 5:ใช) �(1) �(2) �(3) �(4) �(5)
2.11 ระดบความรสกเปนสวนหนงของสงคมของทาน
2.11.1 ทานรสกเปนสวนหนงของสงคม (1:ไม ถง 5:ใช) �(1) �(2) �(3) �(4) �(5)
2.11.2 ทานรสกวาทานสามารถเขาถงทรพยากรของสงคมได (1:ไม ถง 5:ใช) �(1) �(2) �(3) �(4) �(5)
2.12 ระดบความกงวลตอคนแปลกหนา (1:ไมกงวล ถง 5:กงวล) �(1) �(2) �(3) �(4) �(5)
149
3 ความเตมใจทจะใชบรการชวยเหลอในการเดนทางจากอาสาสมคร
3.1 ประเภทของบรการททานสนใจ �รถรบสงถงจดหมาย(1)
□รถรบสงรวมเดนทาง(1.1)
□รถบสรบสงรวมเดนทาง(1.2)
�ผชวยเหลอการใชระบบขนสงสาธารณะ(2)
□ผชวยสอนการเดนทาง บางครง(2.1)
□ผชวยสอนการเดนทาง ทกครง(2.2)
□ผดแลดานความปลอดภยในการเดนทาง(2.3)
□อนๆ(2.4)_______________
�ไมสนใจ(3)
3.1.1 ถาไมสนใจ: มส งอนๆททานอยากใหพฒนาหรอไม �พฒนาบรการของแทกซ(1)
□ขยายพนทใหบรการ(1.1)
□ความนาเชอถอของคนขบ(1.2)
□ลดราคาคาโดยสาร(1.3)
□อนๆ(1.4)_______________
�พฒนาระดบการใชบรการของระบบขนสงสาธารณะ(2)
□ระบบขอมล(2.1)
□ความปลอดภยในการใชบรการ(2.2)
□ความสะดวกสบายบนยานพาหนะ(2.3)
□ลดราคาตว(2.4)
□ความตรงตอเวลา(2.5)
□อนๆ(2.6)_______________
�อนๆ(3)____________________
�ไมตองการ(4)
3.1.2 หากทานเลอกไมตองการในขอ3.1และ3.1.1 โปรดบอกเหตผล �ทานกงวลเรองความปลอดภยของอาสาสมคร(1)
�ไมมคนชวยเหลอในขณะท ากจกรรม(2)
�อนๆ(3)____________________
การเดนทางของทานหลงจากทไดรบการชวยเหลอจากอาสาสมคร
3.2 ชอปปง, พบปะเพอนและญาตตางบาน, กจกรรมยามวางทกประเภท ____________ ครง/สปดาห
3.2.1 จดหมายปลายทางหลก (หากทานเลอกจดหมายปลายทางใหม) ____________
3.2.2 ระยะทาง ____________ กโลเมตร
3.2.3 รปแบบของจดหมายปลายทาง �ใจกลางเมอง(1) �ชานเมอง(2) �แถวบาน(3)
3.3 กจกรรมทเกยวกบหนาทการจดการทกประเภท (เชนการเงนหรอพบแพทย) ____________ ครง/สปดาห
3.3.1 จดหมายปลายทางหลก (หากทานเลอกจดหมายปลายทางใหม) ____________
3.3.2 ระยะทาง ____________ กโลเมตร
3.3.3 รปแบบของจดหมายปลายทาง �ใจกลางเมอง(1) �ชานเมอง(2) �แถวบาน(3)
3.4 ระดบความรสกเปนสวนหนงของสงคมของทาน
3.4.1 ทานรสกเปนสวนหนงของสงคม (1:ไม ถง 5:ใช) �(1) �(2) �(3) �(4) �(5)
3.4.2 ทานรสกวาทานสามารถเขาถงทรพยากรของสงคมได (1:ไม ถง 5:ใช) �(1) �(2) �(3) �(4) �(5)
3.5 วนททานตองการใชบรการ �วนธรรมดา �วนหยด
3.6 กรณากรอกราคา 4 ราคาดานลางในความคดเหนของทาน -ถาเลอก "รถรบสง" กรอกคาโดยสาร
-ถาเลอก "ผชวยเดนทาง" กรอก คาตวผชวย
3.6.1 สมเหตสมผล ____________ บาท/ครง
3.6.2 แพง ____________ บาท/ครง
3.6.3 แพงเกนไปทจะเตมใจใชบรการ ____________ บาท/ครง
3.6.4 ถกเกนไปทจะเตมใจใชบรการ ____________ บาท/ครง**ถกเกนไปหมายถงราคาททานไมใชบรการเพราะวาถกจนทานมความรสกวาบรการนนไมดและมความเสยงทจะไมปลอดภยกบตวทาน
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งานศกษาโครงการอาสาสมครผชวยผสงอายในการเดนทาง (สวน ข)
แบบสอบถามนเปนสวนหนงของงานวจยของคณะวศกรรมศาสตร มหาวทยาลยฮอกไกโด สถาบนเทคโนโลยแหงเอเชย และ จฬาลงกรณมหาวทยาลย ซงงานศกษานมความประสงคทจะศกษาความเปนไปไดในการด าเนนงานโครงการ อาสาสมครผชวยผสงอายในการเดนทาง ทางผวจยมความยนดทจะรบฟงความคดเหนจากทาน โดยการสมภาษณนนจะใชเวลาประมาณ 10 นาท โดยขอมลทกอยางนนจะถกปดเปนความลบ
1 ขอมลทางเศรษฐกจและสงคม
1.1 เพศ �ชาย �หญง
1.2 อาย ____________ ป
1.3 ระดบการศกษา �มธยมตน(1) �มธยมปลาย(2)
�ปวส.(3) �ปวช.(4)
�ปรญญาตร(5) �ปรญญาโท(6)
�อนๆ (________)
1.4 อาชพ �ไมไดท างาน(1) �นกเรยน(2)
�ขาราชการ(3) �พนกงานรษทเอกชน(4)
�ธรกจสวนตว(5) �ฟรแลนซ(6)
�อนๆ (________)
1.5 รายได ____________ บาท/เดอน
1.6 ทานมรถยนตและใบขบขในครองครอง �ใช �ไมใช
2 การเดนทางในปจจบนของทาน
2.1 การเดนทางไปกลบเปนประจ า �วนธรรมดา �วนหยด
2.1.1 จดตนทาง _________________
2.1.2 จดหมายปลายทาง1 _________________
2.1.3 จดหมายปลายทาง2 _________________
2.1.4 รปแบบการเดนทางหลกททานเดนทางเปนประจ า �เดน หรอ จกรยาน(1)
�ขบรถยนต(2)
�รถเมล หรอ รถต(3)
�รถไฟฟา(4)
�แทกซ(5)
2.2 การเดนทางทไมเปนประจ า �วนธรรมดา �วนหยด
2.2.1 จดตนทาง _________________
2.2.2 จดหมายปลายทาง1 _________________
2.2.3 จดหมายปลายทาง2 _________________
2.2.4 รปแบบการเดนทางหลกททานเดนทางเปนประจ า �เดน หรอ จกรยาน(1)
�ขบรถยนต(2)
�รถเมล หรอ รถต(3)
�รถไฟฟา(4)
�แทกซ(5)
2.3.1 เวลาวาง(ในวนท างาน) ____________ ชวโมง/วน
2.3.2 เวลาวาง(ในวนหยด) ____________ ชวโมง/วน
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C) The survey pictures of the Case Study B-2
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APPENDIX C
The chi-square test between elderly age distributions of collected sample and national statistic
A) Age distributions
Table of Age distribution according to the sample and National Statistic Office
Age (year) Frequency as proportion
Sample Population
60-64 50 34
65-69 29 26
70-74 14 15
75-79 4 12
80-84 2 8
85-89 1 3
90-94 0 1
95-99 0 1
Total 100 100
B) Chi-square test
• Null hypothesis (H0): Elderly age distributions of collected sample and national statistic was
similar
• Alternative hypothesis (HA): Elderly age distributions of collected sample and national statistic
was not similar
Table of Chi-square test
Chi-square value Degree of freedom P-value
13.8457 7 0.0540
*：P<0.05 **：P<0.01
Null-hypothesis was accepted that means elderly age distributions of collected sample and national
statistic was similar
