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Science and Information Conference 2013

October 7-9, 2013 | London, UK

Application of Granularised owe framework for

modeling urban Traffic System (UTS) Dynamics by

transforming static traffic objects to live)

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AbstractThe paper provides a framework for modeling Urban

Traffic System using the concept of Granular computing, IoT andCollaborative Agent methodology. The framework is split intoGranules, which are processed through OWL for making it machineunderstandable. The framework provides a real time search

information to the commuters using Web Semantics.

KeywordsIoI OWL, Urban Traffic System (UTS) Granular

Computing)

I. INTRODUCTION (Heading 1)With the lise in population there is sudden opur in travel

needs of people, the impact of spur leads to traffic congestion

which is probably the most perennial problems faces by

various countries. As per the latest survey on Urban Traffic

Mobility (2009) traffic congestion results in loss of 7.3 millionhours of productivity valued at 6200 crores. ( In India).

Since mobality in basic needs of survival congestion

cannot be eliminated completely but necessary steps can be

taken to ease out the traffic conditions : There is significant

researches are going in this direction but unfortunately none

of the model are able to passify the problem of congestion

The previous model like PLOTS, ATLAS and TRANST

developed for vehicles studies only considers limited micromobility, involving restricted vehicle movements but no

attention was paid on micro mobility and its interaction. Thusthere exists a basic need of development of framework which

can cater the micro-mobility aspect focusing on the

behavioural aspect of commuting. This framework will be

able to support the realistic behavioural aspect of urban traffic

congestion.

To develop close to real time simulation of urban Mobility

we will be beldning the concept of Granular computing ,

Internet of Things ( IoT) and Multi Agent Group Behaviourbased on People -Machine Interface.

A. Granular Computing

"Granulation of an object. A leads to the collection of

granules. A, with the granule being a clump of point ( objects)

drawn together by indistinguishability, Similarity, proximity

or functionality" was stated by Zadeh (1997). The main

purpose of granulerisation is to describe important or

interesting patterns in data. In general it aims to discover

meaningful structure in a particular data set. The concept of

granulerisation started for Fuzzy classification of data. Theuse of granulerisation has promoted the necessary flexibility to

represent vague and unprecise linguistic term supporting

meaningful refinement of the data set. Granulerisation

provides generalisation as well as specialisation of the data set

using the concept of linguistic modifiers. It works on two

basic pattern analysis and dissimilarity pattern. There are

various algorithms defined for cluster analysis which are

categorised as : Hierarchical and objective function based.

B. Internet of Things

The concept of Internet of thing in (2003) for tracing the

flow of goods in supply Chain. IoI provides an interfere

between the real world object adn Electronic device often

called as Context of Things. The ubigutons framework helpsin achieving many tasks relating to physical world. Significant

efforts in the area of combining sena information into

communicty information is taking place maintaining high

value of semantic integration and information entrepy. Internet

of Thing is an evolving internet which ranges for object

collaboration which could be as small as pen to as big as a car.

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Figure 1 : IOT Framework

The mix of cloud services, Web services and sensor : networkfrom Internet of things (IOT). Any real world change could be

linked to global network infrastructure and the objects setsthemselves by the help of interperable communication

protocol, thus creating a virtual Intelligent background for any

industry or human services.

C. Collaborative Agents

Collaboration amongst agnt started from the concept of

Multi Agent system. Multi Agent system provides a structured

method of solving complex problem. If we have to colloboratemultiple heterogeneous activities we use coo-operative multi

Agent system. It Involves learning by many Agents

simultaneously. There are various learning schemes being

used in Multi Agent Learning: Supervised, unsupervised and

evolutionary learning. These learning is applied on teams for

collaboration performance. It could be a homogeneous team

learning and heterogeneous team harming. Homogeneous

Team belongs to group of Agents of same cluster. We Authors

will be using the combination of Heterogeneous and

Homogeneous Agents combination of Heterogeneous andHomogeneous Agents in modeling Urban Trafffic system for

colloborative work support.

II. LITERATURE SURVEY

The paper by Li. M. and Chong [80] on "Agent OrientedUrban Traffic Simulation" describes the use of interactionagent in controlling and management of Urban Trafficsystem, the shortcomings with paper wasimproperconnectivity between multiagent and objectmodeling. Zhao & Xin Chen [77] proposed."

Intelligent Cooperation Algorithm highlighting the use ofgeneric reinforcement learning which will be used by authorsin maintaining the hetrogeneity. DESIRE [95] developed byFrances, Nick and Jain provides highlevel modeling

framework enabling both the specification and implementationof systems esigner view to explicitly and precisely specify

both the specification and implementation of systems designerview to explicitly and precisely specify both the ingredientsfunctionality. The paper by R.J.F. Rossetti and S. Bampi [101]shows a synthesis of complex UTS into three types of flows :Vehicle flow, information flow and Decision flow BargielaA., Pedrycz w et al. highlights the issue of Data granulationthrough optimisation of similarity measure." Archives of

Control Sciences, 2002.

Bariela A. et. al. also highlights the "Integeration ofheterogeneous traffic and travel information through acombined Internet and mobile communication". EPSRC GRFinal Report. We are motivated by the papers from Bargiela intransforming IOT based Traffic Informaiton into granules.

III. PROPOSED FRAMEWORK

The Combination Internet of thing (IOT) and Granularcomputing provides a holistic view on Hetrogeneous Controlsystem. For IOT technology we will be using Zigbee Protocolin determining the concentration of Traffic Linking of TrafficLight Cluster and Road Information is done by web 2.0services. The two clusters are being controlled by the help ofcontrol centre (Fig. 2).

Fig. Basic Building Block of Control Centre.

For Modeling the Urban Traffic system we will be usingGranular Ontology language to provide efficient flow ofinformation. Each vehicle is assumed to smart having Zigbeestandard of transmitting the signal.

Fig.3. Layout of SMART Vehicle Monitoring System UsingConcept of Internet of Things (IOT).

Figure 3 shows the schematic arrangement of sensorNetwork. Each erossing is supported by trans receiver attatchedto it which send/receives signal from the nearest ZigBeeTransreceiver Tower. All these towers are connected to theControl Centre. The Control entre has all the Gis relatedinformation covering the main road and love by passes :Consider a situation where there is heavy traffic betwencrossing I and II. There too possible solutions to the problemeither timing of the traffic light should be altered or the trafficneeds to be diverted to the by. The signal is given by the help

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of VMS to the drivers the control centre, which find out bestsuitable. The entire process of information works in real timeand in collaborative mode. The signals general are beinganalysed by the control centre machines, secondly it providesan online access to the commuters. It task-Drivengranulerisation are able to derive application- specificinformation.

Figure 4 : Complete link method of calculating the distance

Figure 4 shows the formation of granules based on the Task-Driven informaiton in real time scenario. The Separation ofTask Driven distance functions between patterns of contorlinformaiton of variety of signals like (Car, Zigbee Tx).

We can simulate the above condition using granularcomputation in MAT LAB (Figure 5) to find Clustering usingFCM.

Figure 5 : Generaiton of Granules using FCM

The dynamic information generated by the Clusters are thenmade machine understandable by the help of OWL. To makeUbquitous understandable by the help of OWL. To makeUbquitous devices colloborate intelligentally we represent theframeowork using knowledge representation and semanticswhich support a more closely knitted and dynamicenvironment. The basic RDF structure consist of.

Figure 5 : RDF of Traffic Monitoring Framework

The Granular Classification in Ontology can be realised byOWL. The category of two granules which are possible are :(1) Lane GIS Granule (2) Sensor Granule (3) Control Granule.

In order to transmit the information in Ubquitous mode andmake it Machine understandable we write a OWL which could

possibly link there granules for faster and accurate processingand support latest information about traffic conditions. Asample OWL which links ZigBee sensors and Control Centreis written as :

< owl : Class rdf : ID = 'ZigBee Sensor'>

The above sample code provides an information that set ofall individuals of class ZigBee sensor is a subset of the set ofall individuals in the class Control Centre.

IV. CONCLUSION

The paper focuses on a framework consisting of Ubquitousenvironment supported by IoT which is classified into

Granules for clustering dynamic information about urbanTraffic. For making the entire process more machineunderstandable we used the concept of OWL as it helps thecommuters in providing intelligent seard.

REFERENCES

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[1] R.Krishapuram, H. Frigui, and O. Nasrovi, "Fuzzy and Possibilistic shellclustering algorithms and their application to boundary detection andsurface approximation", IEEE Trans. fuzzy system. 3(1), 1995.

[2] F. Hoppner, F. Klawonn et. al, "Fuzzy Clster Analysis", Viky, England,1999.

[3] A. Bargiela and W. Pedrycz, "Recursive information granules :":Aggregation and interpretation issues,"IEEE Trans. Syst. Man Cyborn,2003.

[4] A Bargida and W. Pedrycz,"A model of granular data: a design problemwith Tchebysher FCM", soft computing , 2005.

[5] Dieter Uckelmann, Mark Harison, Florian Michahelles,"AnArchitectural Approach Towards the Future Internet of Things",Architecting the Internet of things, Springer Verlag Berlin Heidelberg,2011.

[6] Oridin Vemesan, PeterFriess, "Pan European Cross Fertilisation andIntegration of IoT Programmes and Initiative through National ValueCreation networks", Business-Information Systems, Inernet of thingsEuropean Research Cluster (IERC), 3rd edition of Cluster Book,m 2012.

[7] Adekoya Adebayo Felix, Akinwale Adio Taofiki, Sofoluwe Adetkunbo,"Aconceptual framework for an Ontology- Based Examination system".International Journal of Advanced Computer Science and Applications,Voll. 2, No.5, 2011.

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