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Volume 4 Issue 3 November 2016

International Journal of Informative & Futuristic Research ISSN: 2347-1697 OPTIMUM TRIP PLANNING TO DETOUR TRAFFIC CONGESTION USING VANET BASED TALKING CARS Paper ID

IJIFR/V4/ E3/ 005

Page No.

5440-5456

Subject Area

Keywords

VANET, SUMO, OMNeT++, Veins, Bidirectional coupling

Electronics Engineering

Research Scholar, 1 D.P. Mishra Department of Electronics Engineering, G. H. Raisoni College of Engineering, Nagpur, Maharashtra, India Professor and Head, 2 G.M. Astutkar Department of Electronics & Communication, Priyadarshini Institute of Engineering & Technology, Nagpur, Maharashtra, India Abstract The Intelligent transportation system (ITS) has a major contribution in improving the safety, efficiency and quality of transportation system. Vehicular Ad-hoc Networks (VANETs) have emerged as an exciting research and application area. This opens the possibility of various powerful and high potential life changing applications on safety, efficiency, comfort, cooperation and participation when vehicles are on the road. VANETs have an important feature which can affect people’s life and death decisions. It is not advisable to set up and implement complicated system trials in real world before knowing the impact of all possible parameters used in VANET systems. Most of the current navigation applications, based on Global Positioning Systems (GPS), only use static information for route planning, which have no scope for real time traffic events. The researchers working in the field of VANET are not able to use software tools realistically to assess the applications of VANET. In this work, it has been shown that, if Traffic Simulator (TS) and Network Simulator (NS) are bi-directionally coupled then traffic congestion can be avoided by dynamically re-routing the vehicles. A simulation has been developed using server-client architecture in order to bidirectionally couple TS and NS. The outcome shows reduction in travel time which, in turn, reduces the fuel consumption and environmental pollution. Hence, it is capable of generating realistic simulation of real world traffic problems while testing Inter Vehicular Communication (IVC) networks.

This work is published under Attribution-NonCommercial-ShareAlike 4.0 International License

Copyright©IJIFR 2016 .

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ISSN: 2347-1697 International Journal of Informative & Futuristic Research (IJIFR) Volume - 4, Issue -3, November 2016 Continuous 39th Edition, Page No: 5440-5456 I. INTRODUCTION The fast paced society has now pushed trends and behaviour upon its people, which were considered low occurrence events in the past. Buying a new or personal vehicle is no more a luxury but a primary need. Traffic jams are a regular phenomenon. Increase in traffic jams is directly proportional to commute time, fuel consumption and CO2 emission and causes unnecessary irritation and discomfort to drivers. India is death capital of world with its record average 14 death per hour and an accident in every 4 minutes. Trucks, Lorries and two-wheelers are responsible for 40% fatal accidents. Rush during 10 AM to 12 Noon and in evening hours are more susceptible to fatal accidents. There are long irritating hours of traffic snarl. The government and automotive industries are investing many resources to mitigate the adverse effect of transportation problems [1]. Nowadays, Cellular network, Ad-hoc network, Wireless Sensor Network (WSN), Visible Light Communication, Wi-Fi, and Wi-Max are some of the examples of recent wireless network technologies that have been used in telecommunication, security, location tracking, network monitoring, remote sensing, medicine and education. VANET are the recent challenging technology attracting a lot of research. The goal of VANET research is to develop a communication system for vehicles on the road so as to enable quick and cost efficient transmission of data for passengers’ safety and comfort. Figure – 1 shows the architecture of VANET. This is a generic architecture in which data is transferred among different On Board Units (OBU) and Road Side Units (RSU).Many different and competing design goals have to be taken into account for VANETs to ensure commercial success. When equipped with WAVE (Wireless Access for Vehicular Environment), a novel type of wireless access for vehicle-to-vehicle (V2V) and vehicle-toroadside communications [2].ITS is a wide ranging technology applicable to transportation. It tries to make the transportation system safer, more effective, and reliable and environment friendly, specifically, without altering the existing infrastructure. The field of IVC which includes (V2V) and Vehicle-to-Infrastructure (V2I), also known as VANET, is recognised as an important component of ITS.

Figure 1: Vehicular Ad Hoc Networks.

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ISSN: 2347-1697 International Journal of Informative & Futuristic Research (IJIFR) Volume - 4, Issue -3, November 2016 Continuous 39th Edition, Page No: 5440-5456 Deployment of such a system in real world, for testing purpose, involves high cost and complexity. Hence, the research in VANET relies solely on computer simulation. These simulations have two major components. TS represent the movement pattern of mobile vehicles including its location, velocity and acceleration over the simulation time and NS is responsible for evaluation of data communication between the vehicles. The TS has to be a realistic one that encompasses the salient features of the real world. This can be done by using, the real world map obtained from Topologically Integrated Geographic Encoding and Referencing (TIGER) [3],the data base from US census bureau. The map can also be obtained from Open Street Map (OSM) to simulate a real world scenario [4].Such models opened new avenues for wide range of applications including route planning and traffic safety. Before the use of GPS navigation system, people had to use paper maps. Nowadays, GPS applications have become very popular in our daily life. Navigation system is one of the most used GPS applications. People can plan their routes on an electronic map which helps them in reaching their destination optimally. However, the electronic map information, in general, is static in nature [5]. If a traffic incident or the road maintenance event occurs in real time, the GPS navigation system may lead to an erroneous route resulting in wastage of fuel and time, environmental degradation and the stress associated with driver frustration. Dynamic route planning depends upon two sources of information. First source of information is the recorded traffic information of the road segment that the vehicle has passed through. It is further exchanged through IEEE 802.11 wireless link. Second source of information is provided by Google Map [5]. In current market scenario, GPS navigation applications such as Garmin, Street Pilot for Apple iPhone and PaPaGo! Mobile for Android platform are becoming more and more prevalent [5]. Offline maps are preloaded in these applications, for the user, to look up at addresses and Point Of Interest (POI) such as petrol pumps, restaurants and parking lots without internet coverage. If internet connection is available, then these maps can retrieve real time road and traffic information from internet to plan a faster route to destination by avoiding roads under maintenance or congestion. But unfortunately, all roads do not provide real time information. The problems encountered while using GPS can be remedied using short range vehicle to vehicle communication. When people drive on road, the vehicles can exchange information among them through a dynamically formed VANET. If a vehicle encounters a traffic incident, it can send safety messages to warn neighbouring vehicles with information such as time, location and the status of incident. The neighbouring vehicles then, further forward the safety messages to their neighbours such that they can re-plan their routes dynamically, in real time, in order to avoid encountering the incidents. Bidirectional coupling of NS and TS can show the influence of contribution of control of NS on TS [6]. Presented work indicates that, bi-directional coupling of TS and NS reduces the travel time of some of the vehicles. This reduction eventually leads to actual reduction in fuel usage and CO2emissions. This realistic traffic behaviour cannot be simulated if only TS

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ISSN: 2347-1697 International Journal of Informative & Futuristic Research (IJIFR) Volume - 4, Issue -3, November 2016 Continuous 39th Edition, Page No: 5440-5456 is used for simulating real world traffic conditions. Even if trace files are used for protocol evaluation, they are bound to give erroneous results. Bidirectional coupling gives deeper inside into the impact of network protocols on road traffic behaviour. In addition, results of IVC studies become more realistic. In this work, an attempt has been made to show that a real world conditions can be simulated by bidirectional coupling of vehicle TS and NS. This shows that, the actions of TS can be modified at the behest of communication NS. This gives an opportunity for real time control of vehicles in TS as per the requirement in real world such as traffic incidence. A grid network having 30 vehicles has been designed using SUMO as a proof of concept. OMNET++ is used as network NS. Bi-directional communication is achieved through server client structure of Vehicles in network simulator (Veins). II. RELATED WORK VANET is characterised as special case of Mobile Ad-hoc Network (MANET), which consist of number of vehicles with the capability to communicate with each other without a fixed infrastructure [7]. VANET is a hot area of research encompassing analysis of data, dissemination, study of routing protocols and issues of securities and privacy. The study of TS and their realistic vehicular model deployment is a challenging task [7]. Computer networks cannot be deployed directly in the real world. Perforce computer simulation is used to model computer network configuration. Different network setups are compared using NS [8]. Thus it is possible to recognize and resolve performance problems in the computer network without the need of conducting expensive tests on computer network in real world. There are many open source NS available like NS-2 [9], OMNeT++ [10], JiST/Swans [11]. The commercial one is OPNET. According to granularity with which traffic flows are examined, road TS are classified into Macroscopic, Mesoscopic and Microscopic models [12]. Random Way Point (RWP) is an earlier mobility model widely used in VANET [13]. Computer simulation of traffic is widely used method in research of traffic modelling, planning and development of traffic network and systems. Vehicular traffic systems are of growing concern and interest globally. Modelling arbitrarily complex traffic system is a difficult problem [14]. SUMO [15], Quadstone Paramics Modeller [16], Treiber Micro simulation [17], Aimsun [18], Trafficware SimTraffic [19] and CORSIM TRAFVUN [20] are some example of TS. Out of the six software packages, only two [15, 17] are open source and free to use while remaining are paid. SUMO is being developed by two different institutes DLR and ZAIK, while Treiber’s micro-simulation is a personal project. One of the most popular features of open source software is that they can be further modified by other programmers. In vehicular safety applications, vehicles may generate alert massages to change the mobility pattern of other vehicles in the network. In such cases the NS and TS ought to interact with each other in real time [6]. This problem has been addressed by NCTUns [21]. Network and road traffic simulators are difficult to integrate. In 2006 TraCI was developed by Axel Wegener and his colleagues from University of Lubed [22]. TraCI uses command

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ISSN: 2347-1697 International Journal of Informative & Futuristic Research (IJIFR) Volume - 4, Issue -3, November 2016 Continuous 39th Edition, Page No: 5440-5456 response approach and TCP connection. Later on, it was made available as part of SUMO official release. In 2008, hybrid simulation framework TraNS [23] was developed to integrate SUMO with NS-2. AS TraCI API was changed and TraNS was no longer maintained it works only with very old SUMO versions. Such hybrid simulators which are used in VANET simulations are named as VANET simulator. In iTETRIS project SUMO has been coupled with NS-2’s successor NS-3. Within iTETRIS, the iTETRIS control system is responsible for starting and synchronizing simulators [24]. Veins (Vehicles in network simulator) was developed by Christoph Sommer in 2011 [6], which is a bidirectional simulation framework based on OMNeT++ and SUMO. In 2013, Ing-Chau Chang et. al. developed a VANET based A* route planning algorithm on android platform to find route with shortest travelling time or lowest fuel consumption [5]. In 2014, Oscar Arley Orozco developed “OSA: a VANET application focused on fuel saving and reduction of CO2 emissions” using Veins, OMNeT++ and SUMO [25]. III. MATERIALS & METHOD 3.1 Simulation of Urban Mobility SUMO software has been chosen by the authors to simulate the scenarios in which the vehicles ply on the roadways. This is open source software [15]. It is a macroscopic as well as microscopic continuous TS developed by Institute of Transportation System at the German Aerospace Centre. It can operate at the level of each vehicle. Each vehicle with explicit definition has unique path and identification. Vehicle movements can be described by Origin Destination Matrix (O/D Matrix). Trip file can be generated from O/D Matrix using od 2 trips. For simulation to run SUMO needs network files and road network. Network file define the road map on which the vehicle travels. (a) Importing maps: One can manually generate network file by writing route. But these networks are very basic. It is very difficult to create complex network manually. Some basic networks are Grid Network and Spider Network. But for realistic simulation one has to create network file which represent conditions of real world. SUMO incorporates facility for this purpose. It can handle large simulation maps imported from reality. Initially, SUMO is given with a list of intersections, road segments, traffic control lights, routes and vehicles. One can download real world roadmap from [4]. These downloaded maps are OSM files. OSM file can be edited using Jawa Open Street Map (JOSM) editor. In this step all undesired routes can be removed to simplify the network file. (b) Generation of Network file:After editing OSM file, it can be converted into net.xml file using net convert command line application available in SUMO. Input is .osm file and output is net.xml. (c) Edge: It defines the connection between starting point to destination point. Parameters of Edge are as follows: (i) Id – Gives unique Id to each edge (ii) From – Id of node where edge begins. (iii) To – Id of the node where edge ends. Edge has lanes. Lane parameters are as follows: (i) Id – Unique Id for a lane. (ii) Index – Define sides of lanes e.g. 0 for right

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ISSN: 2347-1697 International Journal of Informative & Futuristic Research (IJIFR) Volume - 4, Issue -3, November 2016 Continuous 39th Edition, Page No: 5440-5456 most (iii) Speed – Maximum speed at which vehicles can travel on it (iv) Shape: Set of coordinates for centre line of lane. (d) Trip File: Creation of Trip file is followed by creation of route on which vehicle travels. This is done in two steps: (i) Creation of trip file. (ii) Creation of route file. Trip file contains the parameters like route Id, departure Id of vehicles, starting lane and ending lane. Creation of trip file is a difficult task. Therefore, SUMO provides Python script called random trips. Using this facility one can create random link between two nodes. Route file contains information about the route, which a vehicle would traverse through. It contains Edge IDs encountered in travel. One can write route files but for large network with high density of vehicles, it is not possible to make route files manually. With trip file and network file as input one can generate route file using duarouter.exe application. Duarouter is part of SUMO suite. SUMO’s TraCI protocol is required to integrate (couple) with it during concurrent simulation in NS-2. TraCI allows the control of vehicles, road, intersections and traffic lights. 3.2 OMNeT++ It is a discrete event NS simulation environment and is available publically since 1997[10]. It is used for simulating communication networks, multi processors and other distributed or parallel systems. It was designed to be a general simulation environment. OMNeT++ has been used successfully in queuing network simulation, wireless ad-hoc network simulations, business process simulation, peer-to-peer network, optical switch and storage area network simulation. The simulator is powerful open source software, which can be used for nonprofit use by academic and research oriented institutions and individuals. OMNeT++ stands in between open source simulation software such as NS-2 and expensive commercial simulator like OPNET. It is available on all common platforms such as Linux, Windows and Mac OS/X. It uses Microsoft visual C++ compiler. It is an extensible, modular component based C++ simulation library and framework. The OMNeT++ simulation IDE (Integrated Development Environment) is based on the Eclipse platform and extends it with new editors, views, wizards and other functionalities. OMNeT++ provides component architecture for models. Components (modules) are programmed in C++. They are then assembled into larger components and models using a high level language NED (Network Descriptor). OMNeT++ is not a simulator per se, but a simulation framework. It provides the infrastructure and tools for writing such simulation. It does not directly provide simulation components for computer networks. Mobility framework or INET framework are needed for specific application areas. These models are developed independent of OMNeT++ and follow their own release cycles. After the first release of OMNeT++ various simulation model were developed for various areas such as wireless or ad-hoc networks, sensor networks, IP& IPv6 network etc. OMNeT++ was designed to support network simulation on large scale. Hence, it needed to support following design requirements: (1) For large scale simulation, it needs to be hierarchical. Model should be developed from reusable components as much as possible. (2) It should be possible to visualize and debug

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ISSN: 2347-1697 International Journal of Informative & Futuristic Research (IJIFR) Volume - 4, Issue -3, November 2016 Continuous 39th Edition, Page No: 5440-5456 simulation. Thus there may be reduction in debugging time. (3) Framework should allow generating and processing input and output files from commonly available software tools. (4) It should be possible to develop model and analyse results. (5) The simulation framework must be itself modular, customizable and allow embedding simulation into larger application. 3.2.1Structure of a Model The structure of OMNeT++ model is shown in Figure 2. A model consists of many modules which communicate using messages. Messages have time stamp and may contain any data. The basic module is called simple module. They are written in C++ using simulation class library. Compound module is made by grouping simple modules. The number of hierarchical levels is not limited. Messages can be sent either via connections or directly to their destination modules. Simple and compound modules are instances of module type. User defines module types while defining a model. Instances of module are used to define complex module type. At last user makes system model as a network model. When module type is used as a building block, there is no distinction whether one is simple or compound module.

Figure 2: Structure of OMNeT++ model. Gates are the input and output interfaces of modules. Messages are sent out through output gate and received through input gate. An output and an input gate are connected through a link called connection. Connections are made within a single level of module hierarchy. In compound module, gates of two sub modules or a gate of one sub module and a gate of compound module can be connected. Connections across hierarchy levels are not permitted, because this would make module reuse impossible. Properties such as propagation delay, data rate and bit error rate can be assigned to connections. It is possible to define connections types with specific properties (termed as channels) and reuse them in several places. Modules can have parameters. Parameters are used to pass configuration data to simple modules. Parameters can take string, numeric or Boolean values. 3.2.2 NED Language In OMNeT++ topology, the structure of a model is defined using descriptive language (NED). NED description includes simple module declaration, compound module definitions and network definitions. Simple module description is used to describe the interface of simple module such as gates and parameters. Compound module definitions include its

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ISSN: 2347-1697 International Journal of Informative & Futuristic Research (IJIFR) Volume - 4, Issue -3, November 2016 Continuous 39th Edition, Page No: 5440-5456 external interface (Gates and parameters) and definitions of sub modules and their interconnections. Network definitions are compound modules that qualify as self-contained simulation modules. NED language has the features like inheritance, interface packages, inner types and metadata annotations. NED has an equivalent XML representation i.e. NED files can be converted into XML or vice-versa without loss of any data. 3.2.3 Graphical Editor The OMNeT++ package is an IDE which contains a graphical editor. NED is its native file format. The editor can also work with hand written NED codes. It is a two way tool. User can edit network topology either graphically or in NED source view and switch between the two at any given point of time. NED is a declarative language and does not use any imperative programming language for defining internal structure of compound module. Most graphical editors only allow use of fixed topologies but due to use of declarative constructs in NED parametric topologies can be built. In many scenarios NED holds advantage over OPNET (commercial simulator, where only fixed topologies can be used), and NS-2 (open source simulator, where building module topologies is done by programming in Tcl). 3.2.4 INI Files In OMNeT++, module behaviour is described by C++ codes, while model topology is described by NED. This arrangement helps in a cleaner model and better tool support. In simulation scenario user wants to know simulation behaviour with different inputs. INI files are used to store these different input values. INI files enable the user to run simulation for each combination of parameters for which s/he is interested. The generated results can be easily processed by built-in analysis tools. 3.2.5 Animation and Tracing It is very easy to debug and trace a simulation models in OMNeT++. Tk env is GUI of OMNeT++. It has three methods: automatic animation, module output windows and object inspectors. Automatic animation does not require any programming and is capable of animating the flow of messages, network charts and reflecting state changes of nodes in the display. Simple modules may write textual or tracing information into special output files. Such outputs appear in module output windows. One can open separate windows for the output of individual modules or modules group. Module output windows simplify the process to follow the execution of simulation programme. Further examination of module output can be done by object inspectors. They are used to display the state or content of an object in proper way. A histogram object is displayed graphically with histogram chart. With OMNeT++ it is automatically possible to inspect every simulation object. There is a facility to turn off the GUI and run the simulation as command line programme. This environment is known as cmd env. 3.2.6 Sequence Chart Most of the time, it is very difficult to understand the behaviour of a large and complex model. Graphical run time environment allows the user to follow the module interaction but up to certain extent only. User can animate, slowdown or single step simulation, but

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ISSN: 2347-1697 International Journal of Informative & Futuristic Research (IJIFR) Volume - 4, Issue -3, November 2016 Continuous 39th Edition, Page No: 5440-5456 sometimes it is insufficient to see the exact sequence of events. OMNeT++ helps user to visualize the sequence of events by recording interaction between modules into a file. OMNeT++ IDE has a tool named sequence chart, which provides a view showing how events follow each other. One can go back and forth in time and filter for modules and events. 3.2.7 Result Analysis Analysis of the results of simulation is a lengthy and time consuming process. Often results are in the form of scalar values, vector values and histograms. The user has to apply statistical methods and tools to extract the important information and then draw some conclusion which needs filtering. In most of the cases the user wants to see the same type of data for each run of simulation or display same graph for different modules. This is a troublesome, boring and tedious process. Hence, there is a strong need for automation. In OMNeT++ result analysis is made rule based. The user selects the input of analysis by specifying file names or file name patterns. Data of interest can be selected into the data base by another pattern rules. Whenever the files or their contents change the data set contents or charts are re-modified. Data in result files are tagged with meta information, such as experiment and measurement levels. This makes filtering process easy. IV. SIMULATION & EXPERIMENTATION 4.1Software Installation To configure Veins between SUMO and OMNeT++, we need to correctly install the platforms/simulators one by one given in Table 1. Table 1: Simulation software and versions Sr. No Software Platform/ Version 1 Linux Ubuntu 12.04 2 Veins Version3.0 3 SUMO Version0.21.0 4 OMNeT++ Version0.21.0 4.2 Software Integration To evaluate protocol performance like AODV, DSDV and DSR, for VANET applications, many researchers use NS-2 or other similar simulators. However, the real life situation is different and difficult to create using NS-2 because it is basically a NS. In case of real world traffic, for VANET application, either NS-2 or SUMO (or other similar software), stand alone, cannot be used. Because of this reason there is a strong need for software integration of NS and TS. This facilitates pragmatic evaluation of VANET application. In view of the above mentioned, some environment is needed which can integrate the features of SUMO and NS-2 simulator. There are some packages designed for such purposes like TraCI, TraNS and MOVE. Currently (in 2015), the use of NS-2.3.5 version

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ISSN: 2347-1697 International Journal of Informative & Futuristic Research (IJIFR) Volume - 4, Issue -3, November 2016 Continuous 39th Edition, Page No: 5440-5456 and SUMO 0.20.0 version is a common practice. Both version of simulator are stable and widely in use. TraCI is the interface developed for integrating SUMO simulator and NS-2 simulator. It was developed in 2008 and the package was no longer maintained. As a result, this package is not supported for current version of simulators. TraNS (Traffic and Network Simulation Environment) is a GUI tool that integrates TS and NS (SUMO and NS-2) to generate realistic simulations inVANETs. TraNS allows the information exchanged in a VANET to influence the vehicle behaviour in the TS. For example, when a vehicle broadcasts information reporting an accident, some of the neighbouring vehicles may slow down. TraNS is maintained upto some extent but later version of TraNS is not compatible with SUMO simulator and NS-2 simulators. MOVE is a tool that allows users to rapidly generate realistic mobility models for VANET simulations. MOVE is built on top of an open source micro TS SUMO [26]. The output of MOVE is a realistic mobility model and can be immediately used by popular NSs such as NS-2 and Qualnet. This tool first generates the traces using SUMO simulator and then can use those traces on NS-2 as input, but both simulator cannot work together simultaneously using MOVE simulator. Veins is an open source framework for integrating TS (SUMO) and NS (OMNeT++). It extends these to offer a comprehensive suite of models for IVC (Inter-Vehicle Communication) simulation. 4.2.1 Veins The Veins framework includes a comprehensive suite of models to make vehicular network simulations as realistic as possible, without sacrificing speed. The GUI and IDE of OMNeT++ and SUMO can be used for quickly setting up and interactively running simulations. Veins has following features: (1) Allows for online re-configuration and re-routing of vehicles in reaction to network packets. (2) Relies on fully-detailed models of IEEE 802.11p and IEEE 1609.4 DSRC/WAVE network layers, including multi-channel operation, QoS channel access, noise and interference effects. (3) Can simulate city block level simulations in real time on a single workstation. (4) Can import whole scenarios from OpenStreetMap, including buildings, speed limits, lane counts, traffic lights, access and turn restrictions. (5) Can employ validated, computationally inexpensive models of shadowing effects caused by buildings as well as by vehicles. (6) Supplies data sources for a wide range of metrics, including travel time and emissions. 4.2.2 Framework of Veins Structure of Veins is shown in Figure 3. Road traffic simulation is performed by SUMO and Network simulation is performed by OMNeT++ simulator. Both simulators are bidirectionally coupled and simulations are performed online. This way, the influence of NS on road traffic can be modelled and complex interactions between both domains can be examined.

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ISSN: 2347-1697 International Journal of Informative & Futuristic Research (IJIFR) Volume - 4, Issue -3, November 2016 Continuous 39th Edition, Page No: 5440-5456

Figure 3: Bi-directional coupling between SUMO and OMNET++ simulators

To perform IVC evaluations, both simulators run in parallel, connected via a TCP socket. The protocol for this communication has been standardized as the Traffic Control Interface (TraCI). This allows bi-directionally coupled simulation of road traffic and network traffic. Movement of vehicles in the road TS SUMO is reflected in movement of nodes in an OMNeT++ simulation. Nodes can then interact with the running road TS, e.g. to simulate the influence of IVC on road traffic. 4.3 Experimentation Extensive experiments have been performed by authors using the simulators described above. However, one specific case is presented here to establish the proof of concept. Three scenarios have been created. Scenario1: Journey of vehicle without accident which uses SUMO only; Scenario 2: Journey with an accident using SUMO only; Scenario 3: Journey with an accident which uses SUMO as well as OMNeT++. All these scenarios are explained below one-by-one. 4.3.1 Scenario 1 A grid network having 30 vehicles has been developed using SUMO. It has junction points with traffic lights. Vehicles start their journey at random time. Here SUMO is working stand-alone and there is no connection with OMNeT++. In this scenario, without coupling, the travel time of all vehicles has been observed to be more or less same to reach their destination. The differences in travel time are due to differential delay at traffic lights. The screenshot of the simulation is depicted in following Figure 4.

Figure 4: Screenshot of Scenario 1.

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ISSN: 2347-1697 International Journal of Informative & Futuristic Research (IJIFR) Volume - 4, Issue -3, November 2016 Continuous 39th Edition, Page No: 5440-5456 4.3.2 Scenario 2 This is also a similar scenario but in this scenarioan accident has been included in the simulation. Due to this accident some of the vehicles have to stop, during their journey,until the accident is cleared. Thus these vehicles could not reach their destination in estimated travel time. However, other vehicles, which are not traveling through the segment in which the accident occurred, reached their destination in scheduled travel time. In this scenario, there is no coupling between TS and the NS. The vehicles en-route the accident did not get prior information about the accident. They could not change their path in order to avoid the segment in which there was an accident. Therefore, they got stuck in the accident route segment. The screenshot of the simulation is depicted in following Figure 5.

Figure 5: Screenshot of Scenario 2.

4.3.3 Scenario 3 This scenario depicts the use of VANET technology in ITS. Using VANET vehicles are informed about their surroundings. They get the information about all the events in real time. Thus, they are able to re-route their plan of travel and avoid the traffic jam due to various reasons. Travel time, fuel consumption gets reduced, and road-rage can be avoided. In this scenario, with bidirectional coupling, some vehicles en-route the road segment in which the accident occurred received the message and rerouted them to reach their destination. These vehicles reached their destination with lesser travel time as compared to the vehicles which got stuck in the road segment where the accident occurred. The screenshot of the simulation is depicted in following Figure 6.

Figure 6: Screenshot of Scenario 3.

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ISSN: 2347-1697 International Journal of Informative & Futuristic Research (IJIFR) Volume - 4, Issue -3, November 2016 Continuous 39th Edition, Page No: 5440-5456 V. OBSERVATION Travel time of all vehicles has been recorded through the simulators in above stated scenarios. It is presented using Bar Graph so that comparison can be made. Travel time (in seconds) without accident (SUMO standalone) i.e. scenario 1, with accident (SUMO standalone) i.e. scenario 2 and with accident using SUMO + VEINS + OMNeT++ i.e. scenario 3 are shown in Figure 7, 8 and 9 respectively. 160 140 120 100 80 60 40 20

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Figure 7: Node (Vehicle) No V/s Travel time Bar Graph of Scenario 1

Scenario 2: Travel time (in Seconds) with accident (SUMO standalone) is shown in Figure 8.

Figure 8: Node (Vehicle) No. V/s Travel time Bar Graph of Scenario 2

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ISSN: 2347-1697 International Journal of Informative & Futuristic Research (IJIFR) Volume - 4, Issue -3, November 2016 Continuous 39th Edition, Page No: 5440-5456 Scenario 3: Travel time (in Seconds) with accident using SUMO + VEINS + OMNeT++ is shown in Figure 9.

Figure 9: Node (Vehicle) No. V/s Travel time Bar Graph of Scenario 3

From the observation of above figures it is clear that there is significant difference in travel time of all three scenarios. Interpretations of these observations are presented in next section. VI. RESULT Define A comparison of travel time in various scenarios has been made through the bar chart depicted in Figure 10.

Figure 10: Comparison of travel time in three scenarios

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ISSN: 2347-1697 International Journal of Informative & Futuristic Research (IJIFR) Volume - 4, Issue -3, November 2016 Continuous 39th Edition, Page No: 5440-5456 It has been observed that, vehicle no 0, 3, 7, 11, 14 and 17 did not get the information of accident before entering the segment in which the accident occurred. Hence, their travel time due to accident got increased. They could not get benefitted by the communication provided by the VANET. While, in case of vehicle no 20, 21, 26 and 29, there has been considerable reduction in their total travel time. This is because; these vehicles received the information about the accident well before entering the segment, in which there is a scheduled accident. Using the above said information vehicles have been able to change their route, so as to detour the accident segment and complete hassle free journey. There is considerable saving in travel time as well as fuel consumption. VII. CONCLUSION Through experimental results it has been concluded that, with the help of bidirectional coupling, the travel time of some of the cars can be reduced. This shows that the mobility pattern of vehicles may change in real life if the vehicles are able to communicate with each other. The dynamic route planning is possible through use of VANET technology. Hence, the use of VANET in ITES has the potential to address the problem of traffic jams, due to some events, during the journey of vehicles. VANET can reduce traffic jams, fuel consumption, associated road rages, driver irritation and frustration. The performance evaluation of VANET protocols such as AODV, DSR etc. will also give different results if the dynamic connection between TS (e.g. SUMO) and NS (e.g. OMNeT++) is available. VIII. REFERENCES [1] Sunil Kumar Singh, Rajesh Duvvuru, Saurabh Singh Thakur. Congestion Control Technique using Intelligent Traffic and VANET. IJCEA. 2014; 4: 35 – 44. [2] Vishal Kumar, Shailendra Mishra, Narottam Chand. Application of VANETs: Present & Future. Communication and Network. 2013; 5: 12-15. [3] Available from: http://www.census.gov/geo/www/tiger/. [4] Available from: www.openstreetmap.org [5] Ing – Chau Chang, Hung – Ta Tai, Feng – Han Yeh et al. A VANET – based A* Route Planning Algorithm for Travelling Time and Energy – Efficient GPS Navigation App. IJDSN. 2013; Article ID 794521:1 – 14. [6] ChristophSommer, Reinhard German, FalkoDressler. Bi-directionally Coupled Network and Road Traffic Simulation for Improved IVC Analysis. IEEE TRANSACTIONS ON MOBILE COMPUTING. 2011; 10: 3 – 15. [7] David R. C., Fabián E. Bustamante.An Integrated Mobility and Traffic Model for Vehicular Wireless Networks. 2nd ACM International workshop on Vehicular Ad-hoc Networks. 2005September;Evanston; IL 60201; USA. [8] J. Heidemann, N. Bulusu, J. Elson et al. Effects of Detail in Wireless Network Simulation.SCS Communication Networks and Distributed Systems Modelling and Simulation Conference. Proceedings; 2000 September 26. [9] L. Breslau, D. Estrin, K. Fall et al.Advances in Network Simulation. IEEE Computer. 2000; 33: 59 -67. [10] A. Varga, R. Hornig. An overview of the OMNeT++ Simulation Environment. 1st international conference on Simulation tools and techniques for communications, networks and systems & workshops. Proceedings; 2008; Brussels, Belgium, Belgium.

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ISSN: 2347-1697 International Journal of Informative & Futuristic Research (IJIFR) Volume - 4, Issue -3, November 2016 Continuous 39th Edition, Page No: 5440-5456 [11] R. Barr, Z.J. Haas, R. van Renesse. JiST: Embedding Simulation Time into a Virtual Machine. EuroSim Congresson Modelling and Simulation. Proceedings; 2004 September 6 - 10; Paris, France. [12] N. Elloumi, H. Haj-Salem, M. Papageorgiou. METACOR: A Macroscopic Modelling Tool for Urban Corridors. Towards an Intelligent Transportation System. Proceedings of the first World Congress on Application of Transport Telematics and Intelligent Vehicle Highway Systems. 1994 November 30 – 3rd December; Paris, France. [13] Gainanu A., Dobre C., Cristea V.A Realistic Mobility Model based on Social Networks for Simulation of VANETs. IEEE 69thVehicular Technology Conference; 2009 April 26 – 29; Barcelona. [14] G. Kotuserveski, K. Hawick. A Review of Traffic Simulation Software. Res. Lett. Inf. Math. Sci. 2009; 13: 35 – 54. [15] SUMO, Project SUMO available fromhttp://sumo.sourceforge.net/. [16] Available from http://www.paramicsonline.com [17] Treiber M. Microsimulation of road traffic application. Available at http://www.trafficsimulation.de [18] TSS: Traffic Simulation System: Aimsun website, available from http://www.aimsun.com/site. [19] [19]Trafficware: Trafficware website, available from http://www.trafficware.com. [20] [20]McTrans Moving Technology: Tsis: Traffic software integrated system web site, available fromhttp://mctrans.ce.ufl.edu/featured/tsis. [21] S.Y. Wang, C.L. Chou, Y.H. Chiu et al. An Integrated Simulation Platform for Vehicular Traffic Communication. IEEE 66th Vehicular Technology Conference. Proceedings: 2007 September 30; Baltimore MD. [22] A. Wegener, M. Piorkowski, M. Raya et al. TraCI: An Interface for Coupling Road Traffic and Network Simulators.11th Communications and Networking Simulation Symposium (CNS'08). Proceedings: 2008April 14 – 17; Ottawa, Canada. [23] M.Piorkowski, M. Raya, A. Lezama Lugo et al. TraNS: Realistic Joint Traffic and Network Simulator for VANETs. ACM SIGMOBILE Mobile Computing and Communication Review. 2008; 12(1): 31 – 33. [24] M. Rondinone, J. Maneros, D. Krajzewicz et al. iTETRIS: a modular simulation platform for the large scale evaluation of cooperative ITS applications. Simulation Modelling Practice and Theory. 2013; 34: 99-125. [25] Oscar Arley Orozco, Gonzalo Llano. OSA: A Vanet Application Focused on Fuel Saving and Reduction of CO2. Revista S&T.2014; 12(29): 25 – 47. [26] MOVE (MObility model generator for VEhicular networks):Rapid Generation of Realistic Simulation for VANET, 2007. Available from: http://lens1.csie.ncku.edu.tw/MOVE/index.htm. Deepak Kumar P. Mishra is a research scholar in Raisoni College of Engineering Nagpur, India and working as Associate Professor in the Department of Electronics Engineering at Manoharbhai Patel Institute of Engineering and Technology, Gondia, India. He has graduated in Electronics Engineering from Baba Saheb Ambedkar Marathwada University, Aurangabad, India, and Post Graduated in Electronics and Control Engineering from BITS, Pilani, India. He has a vast experience in theacademic field. His area of research is Wireless Sensor Network Technology and Intelligent Transportation System. Dr. Gajendra Mahadorao Asutkar is working as Professor and Head, Department of Electronics and Communication Engineering at Priyedarshani Institute of Engineering and Technology, Nagpur, India. He has graduated in Electronics Engineering from RTM Nagpur University and Post Graduate in Electronics Engineering from Visvesarerraya Technical University Belgum, Karnataka, India. He did his Doctoral Research (Ph.D.) Electronics Engineering

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ISSN: 2347-1697 International Journal of Informative & Futuristic Research (IJIFR) Volume - 4, Issue -3, November 2016 Continuous 39th Edition, Page No: 5440-5456 (Wireless Communication) from Visvesarerraya National Institute of Technology Nagpur India. He has 15 years of experience in teaching and industry. He has contributed more than thirty research publications in various national and international journals and conferences. He is alife member of professional bodies like CSI, ISTE, and Member of IEEE. To Cite This Paper [1] Mishra, P.D., Asutkar, M.G. (2016): “Optimum Trip Planning To Detour Traffic

Congestion Using Vanet Based Talking Cars” International Journal of Informative & Futuristic Research (ISSN: 2347-1697), Vol. 4 No. (3), November 2016, pp. 5440-5456, Paper ID: IJIFR/V4/E3/005.

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