QualNet Simulation of VANET Scenario for TLE (Traffic Light Environment) Performance Evaluation

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1 QualNet Simulation of VANET Scenario for TLE (Traffic Light Environment) Performance Evaluation 1 Manjunath P S & 2 Narayana Reddy 1 Dept. of Telecommunications Engineering, BMS College of Engineering 2 Dept of Electronics and Communication Engineering, S V University, Tirupathy, India manjunathps.tce@bmsce.ac.in Abstract - Vehicular Ad-hoc Networks (VANETs) are attracting considerable attention from the research community and the automotive industry to improve the services of Intelligent Transportation System (ITS). As today s transportation system faces serious challenges in terms of road safety, efficiency, and environmental friendliness, the idea of so called ITS has emerged. Due to the expensive cost of deployment and complexity of implementing such a system in real world, research in VANET relies on simulation. The Traffic Light Environment (TLE) on a small section of a road map is simulated with the help of QUALNET in order to understand the significance of VANET in our day-to-day lives. Traffic data from a limited region of road Map is collected to capture the realistic mobility. The realistic mobility model used here considers the driver s route choice at the run time. It also studies the clustering effect caused by traffic lights used at the intersection to regulate traffic movement at different directions. Finally, the performance of the VANET is evaluated in terms of number of sent packets, average unicast throughput and unicast end to end delay as statistical measures for driver route choice with the traffic light scenario. Keywords - ITS, Routing Protocols, TLE, VANET I. INTRODUCTION The vehicle s destination from the source and their turning directions at the intersections, such as right turn, left turn and straight as per their destination were also set according to the driver s route choice at intersection. The driver route choice behavior with traffic lights at the intersections has been simulated for a real world scenario. In this, all possible routes from the source to destination are defined and the driver needs to decide about which route is to be taken from among all possible routes at any intersection. The presence of traffic lights at the intersection regulates the smooth movement of vehicles in different directions and causes clustering effect by forcing the vehicles to stop at intersection when the signal is red. Therefore, the node density at the intersection increased which improves the network connectivity among the peers at intersection, but the improved connectivity deteriorates the packet delivery ratio. To maintain a practical mobility model is not the only criteria. It is also imperative that the VANET network chosen is of lowest overhead, minimum delays and thus maximum efficiency. Vehicular ad hoc networks (VANETs) are a subgroup of mobile ad hoc networks (MANETs) with the distinguishing property that the nodes are vehicles like cars, trucks, buses and motorcycles.[3] VANET systems should be capable of routing vehicles through paths with least distance and stoppage time due to traffic lights. This is done by means of a traffic control signalling system that works hand-in-hand with the VANET network [5].If two service channels are combined to one 20MHz channel the transmission data rate can reach up to 54Mbps. The maximum downlink and the uplink power should be less than 33dBm [4]. IEEE p is an approved amendment to the IEEE standard to add wireless access in vehicular environments (WAVE) which includes data exchange between high-speed vehicles and between the vehicles and the roadside infrastructure in the licensed ITS band of 5.9 GHz ( GHz) [8]. II. REVIEW OF INTELLIGENT ROAD TRAFFIC SIGNALLING SYSTEM (IRTSS). The main aim of an IRTSS is to provide a safe and conflict free movement of vehicles through different roads, junctions and other traffic structures. An intelligent road traffic system can react to change of traffic flows, road layouts and other time based events quicker than a conventional road traffic system. Current 47

2 generation IRTSS control the traffic flows using the traffic lights by controlling its timing, sequences and cycle time. Most of the countries use the standard traffic signalling cycle which consists of three different phases which are green, amber and red. Some different signalling phases may be implemented according to the design of the road and allowable traffic movements at intersections. The control logic specifies the allocated time for each phases. Detection and estimation of real time road traffic is a significant challenge to develop an adaptive traffic signal controller [6]. The proposed IRTSS algorithm is completely different from the previous works that have been developed employing the VANET architecture. Implementation of the IRTSS through a VANET opens up wider opportunities in the area of intelligent road traffic control system. An IRTSS could potentiality optimize the fuel consumption and emission levels of vehicles by improving the traffic flows. A new traffic estimation technique has been developed in accordance to the IEEE p architecture to implement an adaptive signal control mechanism at the intersections. The model uses V2I communications mechanism of the VANET to detect individual vehicle arrival from different lanes and to adaptively change the traffic signalling phases at the intersection with respect to the vehicles density. The optimized adaptive signalling system, vehicular mobility model and communication network model cooperate with each other within the same control platform of a co-simulation model based on OPNET/QualNet. accommodate mobility of the vehicles. Usually the speed of the vehicles in an urban road network can vary from 40km/h to 80 km/h. The latency requirements of the IRTSS are moderate, particularly for the city traffic. For the highest speed in a city for a packet transmission delay of 1 sec the maximum distance a vehicle will travel is only meters. Hence, it is feasible for a VANET based system to accurately obtain traffic information using the on board unit (OBU) within a vehicle. In the next section detail performance evaluation of the VANET is presented. One of the main design issues of the IRTSS is to control the total channel traffic so that QoS (Quality of Service) can be maintained. The idea used in the system design is very simple [4]. A road infrastructure unit known as the RSU is responsible for periodically broadcasting signalling and other road traffic information on the downlink of a communication network. The car on board unit sends vehicle information such as car ID, type, destination/route, etc. via the uplink to the RSU. The OBU supplies information packet via the IEEE802.11p link on the uplink. The RSU supplies the information to the traffic analysis server that controls the traffic signal parameters. For a wide area networked based traffic control system the RSUs are connected by a backbone network where RSUs can exchange traffic information. Fig. 1 : Illustration of road network and communications network for IRTSS. The basic packet transmission mechanism used in the IEEE protocol is the distributed coordination function (DCF). It adopts the carrier sense multiple access collision avoidance (CSMA/CA) method to support the random access scheme for the basic service set (BSS) devices. The DCF can support the ad hoc network without any infrastructure element such as the access point. For applications such as intelligent road traffic signalling system (IRTSS) the VANET needs to Fig. 2 : Working of the IRTSS. The proposed IRTSS contains two phase signaling system. Each phase (P1 & P2) contains green, amber and red phases. The RSU detects the number of vehicles coming from the east and west direction (EB & WB) and selects the critical lane volume, which is denoted as Z. Similarly, the critical lane volume for the south and north (SB & NB) bound is measured, which is denoted as Z2. Amber light duration is calculated by Where y is the amber duration, t is the clearance time (s), d represents the safe deceleration value (m/s2), v is the speed of the vehicle (m/s), g is the gravitational 48

3 force (9.81m/s2), G represents the grade or slope, l is the length of the vehicle (m) and w is the width of the intersection The phase durations of our proposed ITRS is measured the above mentioned equations, where G and R, are green and red signal duration respectively in the two phases. Similarly h is the saturation headway (s) which is the headway of the vehicles in a stable moving platoon, and Lt is the lost time which includes the startup time. determine the coverage and the transmission range of RSUs. For our simulations, we use the QualNet network simulator. With the simulations we pretend to verify successful routing of our test vehicle from the source to destination based on shortest time of travel while maintaining necessary network requirements i.e p. The scenario properties are adjusted considering our map and requirements. We then place necessary devices and other nodes on the map in order to generate traffic and show the movement of our vehicle. Other node properties are also varied to match our requirements. We also place the mobility pattern for each vehicle (node) in the route of our concern. The model after placing nodes i.e. RSUs and OBUs with their mobility patterns would appear like the picture shown below. III. IMPLEMENTATION 3.1 Extraction of Map For creating a real world Map of specific area, some of the existing tools have been used such as Google Earth, ArcGIS 9 (ArcMap version 9.1) and Adobe Dreamweaver CS4. Satellite image of our area has been taken from Google Earth shown in Figure 1a. ArcGIS is basically a suite consisting of a group of Geographic Information System (GIS) software products. Google Earth gives latitude and longitude of a particular location whereas ArcGIS maps those latitudes and longitudes to the required coordinate plane with the desired origin in a Two Dimensional Space. Some of the 2-D Co-ordinates of this Map were not lying in the first quadrant of the 2-D Coordinate plane. In order to obtain all the co-ordinates in the first quadrant, the origin was shifted to an appropriate location. Shifting of the old Co-ordinates (x, y) to a new origin (h, k) is given by : X= x + h; Y= y + k ; Where (X,Y) represents the translated Co-ordinates 3.2 Creation of QualNet Scenario & Timing Models We define a 1km zone that is fully covered by 11 traffic signals each installed with a Road-Side-Unit (RSUs), which represent fixed devices with a Dedicated Short Range Communication (DSRC) radio. The vehicles are equipped with a wireless communication p device that is On-Board-Unit(OBUs), each RSU covers an area of 1000m and they have a common coverage area of around 100m.The channel properties Fig. 3 : An example scenario on QualNet. In the course of our project implementation experiments and surveys were conducted to determine the right traffic models to simulate in a locality of Bangalore City. A small part of Basavanagudi has been selected to simulate the traffic light scenario on. We went to the junctions with high congestion to test our models. We acted as traffic constables and helped in guiding the traffic through the junction with minimum stoppage times, maximum flow rates and best fairness to all the roads.. Based on the above observed parameters we have designed appropriate timing models. We placed eleven traffic lights in the area including those that do not exist. This is the scenario that has been simulated on QualNet as shown in the previous figure. The traffic signal timing model has been shown for the 7 intersections our test car passes before reaching the destination. The timings models of the traffic lights have been configured in such a way that all vehicles have minimum stoppage time contiguous flows and higher priority to emergency services. In the picture below we have showed the timing models of various 49

4 intersections used in our scenario. The numbers mentioned are the times at which each lane gets its green signal. From the picture we can see that in a wired connection the hub placed in the bottom corner acts as the Backbone of the subnet and is analogous to a cloud in wireless network. 4.1 Simulation IV. SIMULATION AND RESULTS (a) (b) (c) Fig.4 (a), (b) & (c) : A few of the Traffic Signals and their timing models in brackets. 3.3 Network Architecture (Subnets) As we mentioned earlier that the various RSUs are interconnected by an Ethernet backbone network and interact with each OBU in its area of coverage by means of a wireless subnet. The wired connections between different RSUs are represented by the blue line. The wireless subnet includes a cloud through which all the other communications take place. Fig. 5 : A finished scenario with VANET architecture completed. Fig. 6 : Description of Routes. The simulation contains three scenarios. The first scenario depicts the traffic status in the absence of any VANET routing or VANET IRTSS. The scenario has a post-scaled run time of 105 hours which indicates the time taken to travel from the indicated source to destination. The only existing system is that of a VANET safety system. All communication here is only of the broadcast form. The second scenario is that of a car that follows a route suggested by the VANET system (from OBU) based on the information sent by the RSUs at the beginning of travel. The criteria is for travel on maximum number of roads with low traffic congestion with minimum distance overhead. This scenario has a post scaling run time of 82 hrs. All RSUs have a maximum propagation distance of 100m. Packets being communicated through are broadcast and unicast in nature. Here the constant bit rate (CBR) applications are sent over UDP at a rate of 512kbps. The third scenario is that of a car that takes a route that is adaptive and based on the signals from each RSU when the OBU enters its area of propagation. Here the destination node sends information to its nearest RSU. Here the simulation run time is of 82 hours after scaling to QualNet time. RSUs have a maximum propagation distance of a 100 m and we use CBR applications over UDP at 512kbps. 50

5 by which OBU s are connected to the RSU s. Even though the car travels a greater distance in this route, the time taken to reach the destination is lesser as it encounters a fewer number of stops. Fig.7 : Simulation of Route 1. In figure the Traffic Model Generator of QualNet creates the dynamic mobility of varying number of vehicular traffic by generating traffic simulation file for simulation. The traffic simulation files have been generated by interfacing traffic flow with traffic lights. Route 1 simulation depicts the movement of the car and the traffic without the VANET Routing System. Fig.9 : Simulation of Route 3. There is another case considered, named Route 4 although it follows the same route as Route 3. The only difference between Route 3 and 4 being the application i.e. in Route 3 we use CBR (Constant Bit-Rate) and in Route 4 we use FTP (File Transfer Protocol). 4.2 Results Route 1 is the simulation of any VANET Routing system and hence has no results. Fig. 8 : Simulation of Route 2. Figure 6.2 shows the simulation of Route 2 which is run with the VANET Routing System. The green arrows indicate packets travelling from one node to the other; each Traffic signal acts as a node and has an in-built RSU. These signals communicate with each other and in turn communicate with our car and the surrounding Traffic. The white pulse indicates the range of each signal which is 100m radius. The simulation of Route 3 is seen in Figure 6.3. The Blue lines indicate a wired network to which all signals are connected to a Hub and acts as the backbone of the entire system. Dotted lines indicate the wireless network Fig. 10 : Total Packets Received in Route 2 Simulation. The above figure shows the total packets sent in Route 2 where all packets are sent only to node 1. All RSUs communicate their traffic densities to node 1 in order to determine the route. 51

6 Fig.11 : Packets Received in Route 3 Simulation. Fig. 14 : Average unicast Throughput in Route 4. Fig. 15 : Comparison of throughput in Route 3 and Route 4. Fig.12 : Comparison of sent packets in Route 2 and 3. The packets sent in Route 3 are more evenly spread out due to the RSUs propagating their timing models to the other RSUs in the route, therefore the Node 1 receives the Timing Models of all RSUs in the Route. As seen from the two images and the comparison line graph above, there is a higher throughput in Route 4 than in Route 3. This is because FTP requires more Packets for Communication than CBR. Fig. 13 : Average unicast throughput in Route 3. Fig. 17 : Average unicast end to end delay in Route 3. 52

7 Fig.18 : Average unicast end to end delay in Route 4. conditions and traffic light states at any point of time. The intelligent traffic signal adopts an adaptive signalling scheme that optimizes the signal durations based on a real-time traffic estimation technique. The IRTSS has been developed based on a simplistic VANET architecture. By adding an input that pertains to the destination address, the system will be able to find the various possible routes and these routes are evaluated in the fields of time distance and traffic congestion. The model can be further developed to implement a wide area traffic control system. In the wide area traffic control system all OBUs will be connected via a fixed backbone network that will allow traffic information over a large area to be distributed to all OBUs resulting better traffic control mechanism. The wide area system will also allow vehicles to inform the OBUs about their final destination. OBUs could use the destination information to calculate load on different roads and possibly load balance traffic on different roads to reduce the congestions. As a part of the future work the research is working on the development of such a wide area traffic control system. VI. ACKNOWLEDGEMENTS Fig.19 : Comparison of unicast end to end delay in Route 3 and Route 4. From the above graph it can be inferred that FTP has a shorter end to end delay than CBR on the same route, yet we can see that FTP requires a greater transmission distance as many of the nodes are not V. CONCLUSION It can be seen from the above parameters that the mechanism used in Route 3 exerts less overhead on the network and will result in less congested communication channels, whereas route 2 uses a mechanism that causes the channels to get blocked. It was also seen that the travel time in Route 2 was greater than Route 3 despite the fact that the predecessor involves a shorter distance and lesser traffic light intersections than Route 3. On the other hand we can say that FTP has an upper hand when compared to CBR in end to end delay and throughput, but loses out in the propagation distance requirement. The paper aims to first generate a grid map for simulation that exists as a matrix of roads and intersections. On this map by placing RSUs (Road Side Units) and simulating moving cars, a Vehicular Ad-Hoc Network is to be established, where the cars exchange road information with the RSUs regarding traffic We thank Nithanth, Leon and Prateek for help extended for compiling the results. We also thank Department of Telecommunication Engineering, BMS College of Engineering for the support extended for procuring the Qualnet Simulator. VII. REFERENCES [1] B S Nithanth, Prateek T, Leon Ashuthosh, Manjunath P S,(2013) TLE Performance Evaluation for VANETS using QualNet Simulation In : First International Conference on Information & Communication Engineering (Volume 4), Bangalore, India. [2] Nidhi & Lobiyal, D.K., (2012) Performance Evaluation of VANET using realistic Vehicular Mobility, N. Meghanathan et al. (Eds.): Vol. 84, CCSIT, Part I, LNICST 84, pp [3] ] M Raya, P Papadimiratos, J P Hubaux, Securing Vehicular Communications, IEEE Wireless Communications, Vol. 13, Oct [4] Khairnar, V.D., Pradhan, S.N,(2010) Comparative Study of Simulation for VANET, IJCA ( ) Volume 4 No.10. [5] Olariu, S., Weigh, M.C.,(2009) Vehicular Networks, from theory to practice, Vehicular Network Book, CRC Press Publishers. 53

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