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ISSN (Print) : 2278-8948, Volume-2, Issue-6, 2013
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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
International Journal of Advanced Electrical and Electronics Engineering (IJAEEE)
ISSN (Print) : 2278-8948, Volume-2, Issue-6, 2013
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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
International Journal of Advanced Electrical and Electronics Engineering (IJAEEE)
ISSN (Print) : 2278-8948, Volume-2, Issue-6, 2013
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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
International Journal of Advanced Electrical and Electronics Engineering (IJAEEE)
ISSN (Print) : 2278-8948, Volume-2, Issue-6, 2013
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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.
International Journal of Advanced Electrical and Electronics Engineering (IJAEEE)
ISSN (Print) : 2278-8948, Volume-2, Issue-6, 2013
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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.
International Journal of Advanced Electrical and Electronics Engineering (IJAEEE)
ISSN (Print) : 2278-8948, Volume-2, Issue-6, 2013
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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.
International Journal of Advanced Electrical and Electronics Engineering (IJAEEE)
ISSN (Print) : 2278-8948, Volume-2, Issue-6, 2013
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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.
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