qualnet simulation of vanet scenario for tle (traffic ... · pdf fileissn (print) : 2278-8948,...

ISSN (Print) : 2278-8948, Volume-2, Issue-6, 2013

47

QualNet Simulation of VANET Scenario for TLE (Traffic Light

Environment) Performance Evaluation

1Manjunath P S &

2 Narayana Reddy

1Dept. of Telecommunications Engineering, BMS College of Engineering

2 Dept of Electronics and Communication Engineering, S V University, Tirupathy, India

E-mail : [email protected]

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 802.11p is an approved amendment to

the IEEE 802.11 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 (5.85-5.925 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

mailto:[email protected]

International Journal of Advanced Electrical and Electronics Engineering (IJAEEE)


48

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 802.11p 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.

Fig. 1 : Illustration of road network and communications

network for IRTSS.

The basic packet transmission mechanism used in

the IEEE 802.11 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

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 22.22 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. 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



49

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 start-

up time.

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

802.11p 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

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.

802.11p.

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.

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



50

intersections used in our scenario. The numbers

mentioned are the times at which each lane gets its

green signal.

(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.

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.

IV. SIMULATION AND RESULTS

4.1 Simulation

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.



51

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. 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

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.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. 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.



52

Fig.11 : Packets Received in Route 3 Simulation.

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.

Fig. 13 : Average unicast throughput in Route 3.

Fig. 14 : Average unicast Throughput in Route 4.

Fig. 15 : Comparison of throughput in Route 3 and Route 4.

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. 17 : Average unicast end to end delay in Route 3.



53

Fig.18 : Average unicast end to end delay in Route 4.

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

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

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. 477–489.

[3] ] M Raya, P Papadimiratos, J P Hubaux, “Securing

Vehicular Communications”, IEEE Wireless

Communications, Vol. 13, Oct 2006.

[4] Khairnar, V.D., Pradhan, S.N,(2010)

“Comparative Study of Simulation for VANET ”

, IJCA (0975 – 8887) Volume 4– No.10.

[5] Olariu, S., Weigh, M.C.,(2009) “Vehicular

Networks, from theory to practice”, Vehicular

Network Book, CRC Press Publishers.



54

[6] Huang, C.M., Chen, J.L., Chang, Y.C.,(2010)

“Telematics Communication Technologies and

Vehicular Networks”, Wireless Architectures and

Applications, Information Science

Reference,New York Publisher.

[7] Paier, A., Bernadó, L., Karedal, J., Klemp, O.,

Kwoczek, A.,(2010) “Overview of vehicle-

tovehicle radio channel measurements for

collision avoidance applications”, In: 71st IEEE

Vehicular Technology Conference, VTC Spring

Taipei.International Journal of Wireless &

Mobile Networks (IJWMN) Vol. 4, No. 1,

February 2012 248

[8] Lan, K.C., Chou, C.M.(2008) “Realistic mobility

models for Vehicular Ad hoc Network (VANET)

simulations”, In: 8th IEEE International

Conference, pp. : 362 – 366,ITS

Telecommunication.

[9] David R.C., Fabi´an E. B.(2005) “An integrated

mobility and traffic model for vehicular wireless

networks”, In: 2nd ACM International Workshop

on Vehicular Ad Hoc Networks (VANET),

Cologne, Germany.

qualnet simulation of vanet scenario for tle (traffic ... · pdf fileissn (print) : 2278-8948,...

Documents