5_prakiraan Dlm Penerbangan

5/25/2018 5_prakiraan Dlm Penerbangan

1/52

PRAKIRAAN DALAM

PENERBANGAN DANPERENCANAAN

BANDAR UDARA


2/52

FORECASTING FOR AIRPORT PLANNING

DATA YANG HARUS DIKUMPULKAN UTK MENGHITUNG

JUMLAH PESAWAT, PENUMPANG DAN BARANG PADA

MASA DEPAN :

1. Daerah yang dilayani oleh bandara, yaitu daerah operasibandara.

2. Asal dan tujuan perjalanan baik dari penduduk maupunbukan penduduk daerah sekitar bandara tsb, yaitu daerah

pasar lalu-lintas udara.

3. Sifat-sifat demografi dan pertumbuhan jumlah pendudukdari daerah yang akan dilayani bandara tsb.


3/52

FAKTOR-FAKTOR YANG HARUS

DIPERTIMBANGKAN DALAM PRAKIRAAN


JUMLAH PESAWAT, PENUMPANG DAN BARANG PADA

MASA DEPAN :

4. Keadaan ekonomi dari daerah yg ditunjukkan oleh faktor2berikut :

a. Tingkat pendapatan yg dihabiskan perkapita

b. Jenis dan tingkat perdagangan, sifat dan jumlah tenaga

kerja yg dipekerjakan dlm industri2 yg ada.

c. Penjualan perdagangan eceran dan grosir serta deposito

bank

d. Pemesanan tempat di hotel dan motel.


4/52




JUMLAH PESAWAT, PENUMPANG DAN BARANG PADAMASA DEPAN :

5. Kecendeerungan dalam kegiatan transportasi yg sdh adautk penumpang, barang, perusahaan dan pengiriman, dan

surat yg dilakukan dng berbagai cara6. Kecenderungan dalam lalu-lintas nasional, termasuk

dugaan peubah2 yg mempengaruhi pengembangan

dimasa y.a.d.

7. Jarak, jumlah penduduk dan sifat industri dari daerah2 sisekitarnya yg mempunyai pelayanan udara


5/52




JUMLAH PESAWAT, PENUMPANG DAN BARANG PADAMASA DEPAN :

8. Faktor2 geografis yg mempengaruhi kebutuhan2transportasi

9. Tingkat persaingan diantara perusahaan penerbangan dandiantara berbagai cara transportasi yg menyangkut biaya,

waktu perjalanan, frekuensi pelayanan, dll.


6/52

Level of Forecasting

In economics, forecasting is done on two levels,

aggregate forecasting and disaggregate forecasting, and

the same holds true in aviation. From the inception of the

planning process for an airport consideration is given at

both levels. In airport planning, the designer must viewthe entire airport system as well as the airport under

immediate consideration. Aggregate forecasts are

forecasts of the total aviation activity in a large region

such as a country, state, or metropolitan area. Typicalaggregate forecasts are made for such variables as the

total revenue passenger-miles, total enplaned

passengers, and the number of aircraft operations,

aircraft in the fleet, and licensed pilots in the country.


7/52

Level of ForecastingDalam ilmu ekonomi, ada 2 tingkat prakiraan (forecast), yaitu

prakiraan gabungan dan prakiraan dng memisahkan. Hal ygsama berlaku di dunia penerbangan. Dari permulaan proses

perencanaan utk hal2 yang patut dipertimbangkan diberikan

oleh kedua level prakiraan tsb.

Dalam perencanaan bandara, si perencana harus melihatkeseluruhan dari sistem bandara sebagaimana bandara tsb

dilihat memiliki pertimbangan kebutuhan yg segera.

Prakiraan gabungan adalah prakiraan dari keseluruhan

aktifitas penerbangan dalam skala besar seperti negara,

provinsi dan daerah metropolitan. Tipe dari prakiraan

gabungan dibuat utk beberapa variabel spt jumlah

pengembalian dari jarak-penumpang, jumlah penumpang

pesawat terbang, dan jumlah pesawat yg beroperasi, jumlah

pesawat dalam armada, lisensi pilot dlm negara tsb.


8/52

Level of Forecasting

Disaggregate forecasts deal with the activity at individual

airports or on individual routes. Disaggregate forecasts for

airport planning determine such variables as the number of

originations, passenger origin-destination traffic, the number

of enplaned passengers, and the number of aircraft

operations by air carrier and general aviation aircraft at anairport. Separate forecasts are usually made, depending on

the need in a particular study, for cargo movements,

commuter service, and ground access traffic. These

forecasts are normally prepared to indicate annual levels ofactivity and are then disaggregated for airport planning

purposes to provide forecasts of the peaking characteristics

of traffic during the busy hours of the day, days of the week,

and months of the year


9/52

Level of ForecastingPrakiraan yg memisahkan berhubungan dengan aktivitas dari satu

buah bandara atau pada satu rute penerbangan. Prakiraan

memisahkan untuk perencanaan bandara ditentukan olehbeberapa variabel seperti jumlah dari organisasi2, lalu-lintas

asal-tujuan penumpang, jumlah penumpang yg naik pesawat,

dan jumlah dari pesawat yg beroperasi dari operator

penerbangan khusus dan penerbangan umum pada suatu

bandara.

Prakiraan yg terpisah biasanya dibuat, tergantung dari kebutuhan

di dalam studi tertentu, untuk pergerakan kargo(barang),

pelayanan komuter, dan lalu-lintas akses di darat. Prakiraan ini

secara normal disiapkan untuk menerangkan/menunjukkantingkat aktivitas tahunan dan kemudian dipilah untuk

kepentingan perencanaan bandara untuk memberikan

prakiraan dari karakteristik puncak dari lalu-lintas selama jam-

jama sibuk utk harian, harian dalam mingguan, dan bulanandari tahunan.


10/52

Tipe Metode Forecasting

There are four major methods:

Time series method

Market share method Econometric modeling

Simulation modeling


11/52

TIME SERIES METHODTime series analysis or extrapolation is based upon an

examination of the historical pattern of activity and assumesthat those factors which determine the variation of traffic in

the past will continue to exhibit similar relationships in the

future. This technique utilizes times series type data and

seeks to analyze the growth and growth rates associated

with a particular aviation activity

Analisis deret waktu atau ekstrapolasi didasarkan dari

pengujian pola historis dari aktivitas dan asumsi bahwa

faktor2 tsb yang menentukan variasi dari lalu-lintas di masa

lalu dan akan menerus menunjukkan hubungan yg mirip dimasa yang akan datang. Teknik ini memanfaatkan tipe data

rangkaian waktu dan berusaha untuk menganalisa

pertumbuhan dan laju pertumbuhan yg berhubungan

dengan aktivitas penerbangan tertentu.


12/52

TIME SERIES METHODTime series analysis or extrapolation is based upon an

examination of the historical pattern of activity and assumesthat those factors which determine the variation of traffic in

the past will continue to exhibit similar relationships in the

future. This technique utilizes times series type data and

seeks to analyze the growth and growth rates associated

with a particular aviation activity

Analisis deret waktu atau ekstrapolasi didasarkan dari

pengujian pola historis dari aktivitas dan asumsi bahwa

faktor2 tsb yang menentukan variasi dari lalu-lintas di masa

lalu dan akan menerus menunjukkan hubungan yg mirip dimasa yang akan datang. Teknik ini memanfaatkan tipe data

rangkaian waktu dan berusaha untuk menganalisa

pertumbuhan dan laju pertumbuhan yg berhubungan

dengan aktivitas penerbangan tertentu.


13/52

TIME SERIES METHOD


14/52

TIME SERIES METHOD


15/52

TIME SERIES METHOD


16/52

TIME SERIES METHOD

The inability of time series techniques to show a

causal relationship between the dependent and

independent variables is a serious disadvantage.

This is particularly true because, in the absence of

such relationships, the degree of uncertainty insuch forecasts increases with time. However, the

time series method is useful for short-term

forecasts in which the response to changes in those

factors which stimulate the dependent variables isusually less dynamic. In those cases where cyclic

variations may be expected to occur, time series

methods may also be quite beneficial.


17/52

TIME SERIES METHOD

Ketidakmampuan dari teknik deret/rangkaian waktu untukmemperlihatkan hubungan yg bersifat sebab akibat antara

variabel tetap dan variabel bebas adalah kerugian yg

sangat serius. Hal ini dalam kondisi tertentu benar karena,

di dalam ketiadaan dari hubungan tersebut, tingkat

ketidakpastian prakiraan sedemikian shg meningkat thd

waktu. Bagaimanapun juga, metode deret/rangkaian waktu

berguna untuk prakiraan jangka pendek dimana dpt

merespon perubahan2 yg disebabkan faktor2 yg didorong

variabel2 tetap dimana biasanya tdk begitu dinamis. Padakasus2 semacam itu ketika variasi2 yg berhubungan

dengan perputaran dapat diharapkan terjadi, metode deret

waktu dapat pula agak bermanfaat.


18/52

MARKET SHARE METHOD

Forecasting techniques which are utilized to proportion alarge-scale aviation activity down to a local level are called

market share, ratio, or top-down models. Inherent to the use

of such a method is the demonstration that the proportion of

the large-scale activity which can be assigned to the local

level is a regular and predictable quantity. This method hasbeen the dominant technique for aviation demand

forecasting at the local level and its most common use is in

the determination of the share of total national traffic activity

which will be captured by a particular region, traffic hub, orairport.


19/52

MARKET SHARE METHODTeknik-teknik prakiraan yg digunakan utk memecah kegiatan

penerbangan skala besar menjadi skala lokal disebutmodel-model bagian pasar (market share), rasio atau top-

down. Yang berkaitan dengan penggunaan metode

semacam ini adalah peragaan bahwa bagian kegitan skala

besar yg dpt diserahkan kpn tingkat lokal adalah besaranyg tetap dan dpt diperkirakan. Metode ini telah menjadi

teknik yg banyak dipakai utk memprakirakan permintaan

penerbangan di tingkat lokal dan kegunaannya yg paling

umum adalah penentuan dalam kegiatan lalu-lintas

nasional total yg akan ditampung oleh daerah, pusat

kegitan lalu-lintas atau bandara tertentu.

Data historis dipelajari utk menetapkan rasio dari lalu-lintas

bandara lokal thd lalu-lintas nasional total, dan

kecenderungan yg dipastikan.


20/52

MARKET SHARE METHOD

These methods are particularly useful in applications in which itcan be demonstrated that the market share is a regular,

stable, or predictable parameter. For example, the number

of annual enplaned passengers at major air traffic hubs has

been shown to be a consistent and relatively stable factor

and, therefore, this method is often used to predict this

parameter.

Metode ini terutama berguna dalam pengaplikasian dimana

bisa didemonstrasikan bahwa pembagian pasar tsb teratur,

stabil, atau parameternya terprediksi. Sebagai contohjumlah laporan tahunan penumpang yg naik pesawat pada

jalur lalu-lintas utama telah terbukti konsisten dan faktor yg

relatif stabil, utk itu, metode ini sering digunakan utk

memprediksi parameter ini.


21/52

MARKET SHARE METHOD

These methods are particularly useful in applications in which itcan be demonstrated that the market share is a regular,

stable, or predictable parameter. For example, the number

of annual enplaned passengers at major air traffic hubs has

been shown to be a consistent and relatively stable factor

and, therefore, this method is often used to predict this

parameter.

Metode ini terutama berguna dalam pengaplikasian dimana

bisa didemonstrasikan bahwa pembagian pasar tsb teratur,

stabil, atau parameternya terprediksi. Sebagai contohjumlah laporan tahunan penumpang yg naik pesawat pada

jalur lalu-lintas utama telah terbukti konsisten dan faktor yg

relatif stabil, utk itu, metode ini sering digunakan utk

memprediksi parameter ini.


22/52

MARKET SHARE METHOD

Quite often the application of the market share technique isa two step process in which a ratio is applied to

disaggregate activity forecasts from a national to a regional

level and then another ratio is applied to apportion the

regional share among the airports in the region.

Cukup sering aplikasi dari teknik pembagian pasar adalah

proses dua langkah dimana suatu rasio diaplikasikan untuk

prakiraan2 pemisahan aktifitas dari nasional menjadi tingkat

regional/daerah dan kemudian rasio lainnya diaplikasikan

untuk membagikan secara adil pada daerah yg dibagiantara badara di dalam daerah tersebut.


23/52

MARKET SHARE METHOD

An illustration of the application of a market share analysis isgiven in Example Problem 5-2.


24/52

MARKET SHARE METHOD

Example Problem 5-2 The historical data shown in Table5-1 could also be used to prepare a forecast of the annual

passenger enplanements at the study airport in the design

years 2010 and 2015 using a market share method.

In applying the market share method to these data, a top-down forecast technique will be used. The implicit

assumption in such a technique is that the same relationship

between regional annual enplanements and the annual

enplanements at the study airport will be maintained in the

future. To prepare such a forecast, a projection of thepercentage of the regional annual enplanements captured at

the study airport is performed and then a forecast is made of

the regional annual enplanements.


25/52

MARKET SHARE METHOD

Dalam penerapan metode pembagian pasar utk data tsb,teknik prakiraan top-down akan dipakai. Asumsi yg tersirat

dlm teknik seperti ini adalah yg mempunyai hubungan yg

sama antara laporan tahunan regional penumpang pesawat

dan laporan tahunan pd studi bandara akan

ditangani/dipertahankan di masa yg akan datang. Untuk

menyiapkan prakiraan semacam itu, suatu proyeksi dari

persentase laporan penumpang pesawat tingkat regional

ditangkap pada studi bandara yg dilakukan dan kemudian

prakiraan tsb dibuat dari laporan tahunan penumpangpesawat tingkat regional.


26/52

MARKET SHARE METHOD

The study airport forecast percentage is applied to theregional forecast to arrive at the forecast of the study airport

annual enplanements in the design years. A plot of the trend

in the percentage of regional annual passengers enplaned

at the study airport is given in Fig. 5-2.

Studi persentase prakiraan bandara diterapkan untuk

prakiraan tingkat daerah untuk keberangkatan pada

prakiraan studi bandara laporan tahunan penumpang

pesawat di dalam desain tahunan. Sebuah plot dari

kecenderungan dipresentasikan dari laporan tahunanpenumpang pesawat pada studi bandara spt diberikan

dalam Gb. 5-2.


27/52

MARKET SHARE METHOD

Because the variations shown in Fig. 5-2 often make itdifficult to determine if trends may exist, a smoothing

function is often applied to the data to assist in identifying

trends which may not be obvious. In this case, a smoothing

of the data was obtained by computing a running 3-year

average of the data points. As is shown in Fig. 5-3, this

tends to smooth out the variations in the original data and

more readily identifies trends in these data.


28/52

MARKET SHARE METHOD


29/52

MARKET SHARE METHOD

Though it may not be apparent in the original plot, thesmoothing mechanism does indicate a very slight upward

trend in the percentage of regional annual passengers

captured by the study airport. This trend is shown by the

dashed line in Fig. 5-3. This trend line, when projected to the

design years, indicates a forecast of 3.70 percent in the year

2010 and 3.75 percent in the year 2015 as the proportion of

regional annual enplanements forecast to be captured by

the study airport.


30/52

MARKET SHARE METHOD

Meskipun mungkin tdk terlihat dalam plot aslinya,mekanisme membuat mulus tdk mengindikasikan

kecenderungan sangat keatas di dalam persentase dari

laporan penumpang tahunan yg ditangkap oleh studi

bandara. Kecenderungan ini diperlihatkan oleh garis putus2

dalam G.b. 5-3. Garis kecenderungan ini, ketika proyeksi utk

desain tahunan, mengindikasikan prakiraan dari 3,7o persen

pada tahun 2010 dan 3,75 persen pada tahun 2015 sbg

proporsi prakiraan dari laporan tahunan penumpang

pesawat regional yg ditangkap oleh studi bandara.


31/52

ECONOMETRIC MODELING

The most sophisticated and complex technique in airport

demand forecasting is the use of econometric models. Trendextrapolation methods do not explicitly examine the

underlying relationships between the projected activity

descriptor and the many variables which affect its change.

There are a wide range of economic, social, market, andoperational factors which affect aviation.


32/52


Teknik yg paling canggih dan rumit dalam prakiraan

permintaan bandara dan penerbangan adalah penggunaanmodel ekonometrik. Metode kecenderungan ekstrapolasi tdk

dng tegas menyelidiki hubungan2 antara pendeskripsi

aktivitas proyeksi dan berbagai macam variabel2 yg dpt

membawa efek perubahan. Terdapat rentang yg sangatlebar dari faktor2 ekonomi, sosial, pasar dan opersional yg

dpt mempengaruhi dunia penerbangan


33/52


Therefore, to properly assess the impact of predicted

changes in the other sectors of society upon aviationdemand and to investigate the effect of alternative

assumptions on aviation, it is often desirable to use

mathematical techniques to study the correlations between

dependent and independent variables. Econometric modelswhich relate measures of aviation activity to economic and

social factors are extremely valuable techniques in

forecasting the future.


34/52


Oleh karena itu, utk menilai secara tepat dampak

perubahan-perubahan yg di ramalkan di dalam sektor2sosial lainnya thd permintaan dunia penerbangan dan utk

menyelidiki pengaruh pengandaian alternatif diasumsikan

dlm penerbangan, seringkali dikehendaki utk menggunakan

teknik matematika utk melakukan studi hubungan antaravariabel tetap dan variabel berubah. Model-model

Ekonometrik yg menghubungkan ukuran2 kegiatan

penerbangan dng faktor2 sosial dan ekonomi merupakan

teknik yg sangat berguna dalam mempuat prakiraan di

masa yang akan datang


35/52



36/52


There are a great variety of techniques which are used in

econometric modeling for airport planning. Classical tripgeneration and gravity models are quite common in

forecasting passenger and aircraft traffic. Simple and

multiple regression analysis techniques, both linear and

nonlinear, are often applied to a great variety of forecastingproblems to ascertain the relationships between the

dependent variables and such explanatory variables as

economic and population growth, market factors, travel

impedance, and intermodal competition.

The form of the equations used in multiple linear regression

analysis is given in Eq. (5-1).


37/52



38/52


There are a great variety of techniques which are used in

econometric modeling for airport planning. Classical tripgeneration and gravity models are quite common in

forecasting passenger and aircraft traffic. Simple and

multiple regression analysis techniques, both linear and

nonlinear, are often applied to a great variety of forecastingproblems to ascertain the relationships between the

dependent variables and such explanatory variables as

economic and population growth, market factors, travel

impedance, and intermodal competition.

The form of the equations used in multiple linear regression

analysis is given in Eq. (5-1).


39/52


An illustration of the application of simple linear regression

analysis is presented in Example Problem 5-3.

Example Problem 5-3 The historical data shown in Table

5-1 could also be used to prepare a forecast of the annual

passenger enplanements at the study airport in the designyears 2010 and 2015 using a simple regression analysis.

In applying simple regression analysis to these data, let us

assume that a relationship between the study airport annual

enplanements (ENP) and the study area population (POP) isto be examined. Therefore, it is assumed that a linear

relationship of the form shown in Eq. (5-1) exists between

the variables.


40/52


ENP = a0 + a1(POP)

Using a standard regression analysis computer program the

relationship is found to be

ENP = 3,047,032 + 13.8633(POP)

where the coefficient of determination R2 is 0.983815, the

coefficient of correlation is 0.991874, and the standard errorof the estimate, yestis 55,520.9. The regression line and the

data points upon which this regression line is based are

shown in Fig. 5-5.


41/52



42/52


The coefficient of determination indicates that there is an

extremely good relationship between the annualenplanements at the study airport and the study area

population, that is, 98.4 percent of the variation in the study

airport annual enplanements is explained by the variation in

the study area population. Kooefisien determinasi mengindikasikan bhw ada hubungan

yang sangat baik antara laporan penumpang pesawat

tahunan pada studi bandara tsb dan studi wilayah populasi,

menunjukkan bhw 98,4% dari variasi di dalam studipenumpang pesawat tahunan bandara menjelaskan dng

variasi dlm studi wilayah populasi.


43/52


The standard error of the estimate, however, indicates that

there is a large range of error associated with forecastingwith this equation, that is, there is a 68 percent probability

that the forecast of annual enplanements at the study airport

will have an error range of 55,520.9 annual

enplanements. This may or may not be too high dependingon the level of annual operations forecast in the future and

the sensitivity of various components of the airport system to

such variations.



44/52


Standar eror dari estimasi, bagaimanapun, mengindikasikan

bhw ada berbagai macam kesalahan yg terkait dngperhitungan prakiraan, menunjukkan bhw ada 68 persen

kemungkinan bhw prakiraan dari jumlah penumpang

pesawat. tahunan pada studi bandara akan memiliki tingkat

eror dari55,520.9 penumpang pesawat tahunan. Inimungkin atau tdk mungkin terlalu tinggi tergantung pada

tingkat prakiraan operasional tahunan dari sistem bandara

untuk variasi semacam itu.



45/52


Using a trend projection, it is forecast that the area

population in the year 2010, as shown on Fig. 5-6, isexpected to be 363,000. The forecast of the annual

enplanements at the airport in the year 2010 can be found

by substitution into the regression equation yielding

1,985,300 annual enplanements. Similarly, if the forecast ofthe area population in the year 2015 is expected to be

410,000, then the forecast of the annual enplanements at

the airport in the year 2015 is found to be 2,636,900.



46/52


Menggunakan kecenderungan proyeksi, prakiraan ini

menunjukkan wilayah populasi pada th.2010, sptditunjukkan pd. Gb.5-6 diperkirakan menjadi 363,000.

Prakiraan jumlah penumpang pesawat tahunan pd bandara

tsb pd tahun 2010 dpt dicari dng subtitusi ke dalam

persamaan regresi menghasilkan 1,985,300 jumlahpenumpang pesawat tahunan. Mirip, jika perkiraan dari

populasi wilayah pada th.2015 diharapkan mencapai

410,000, kemudian prakiraan jumlah penumpang pesawat

tahunan pada bandara tsb pd th.2015 di dapat menjadi

2,636,900



47/52


Given the range in the standard error of the estimate, it

could be expected that in the year 2010 there is a probabilityof 68 percent that the forecast could range between

1,985,300 55,500, or from 1,929,800 to 2,040,800 annual

enplanements about 68 percent of the time. Similarly, it

could be expected that in the year 2015 the forecast couldrange between 2,636,900 55,500, or from 2,581,900 to

2,692,400. It is likely that this range in the forecasts is

acceptable since it represents about a 2 to 3 percent error.



48/52


FORECASTING REQUIREMENTS AND


49/52


APPLICATIONS

Kebutuhan dan Penggunaan Prakiraan

The specific forecasting needs depend on the nature and

scope of the study being undertaken. The requirements for a

state aviation system plan are very different than those

required for an airport master plan.

Secara spesifik prakiraan dibutuhkan tergantung pada sifatdan ruang lingkup studi yang sedang dilakukan. Kebutuhan-

kebutuhan untuk rencana sistem penerbangan negara

sangat berbeda dari yang dibutuhkan untuk suatu rencana

induk bandara .



50/52


APPLICATIONS

Facility planning requires projections of the parameters

which determine physical design whereas financial planningrequires projections of the cost elements and revenue

sources associated with physical development. This section

outlines the general forecasting requirements for various

types of airport studies and discusses the more commonmethodologies used to arrive at these requirements.

Proyeksi kebutuhan rencana fasilitas dari parameter2

dimana ditentukan oleh desain fisik sedangkan proyeksi

rencana kebutuhan keuangan dari elemen biaya dansumber pendapatan terkait dengan pembangunan fisik.

Bagian ini menguraikan kebutuhan prakiraan secara umum

untuk berbagai macam tipe studi bandara dan didiskusikan

metodologi yg lebih umum digunakan untuk sampai pada

persyaratan ini.

Typical Air Transportation Forecast Variables and


51/52

Typical Air Transportation Forecast Variables and

Their Use in

Aviation and Airport Planning

Typical Air Transportation Forecast Variables and Their


52/52

Use in

Aviation and Airport Planning

5_prakiraan Dlm Penerbangan

Documents

Transcript of 5_prakiraan Dlm Penerbangan