03-Event Notification in VANET with Traffic Congestion Detection and Congestion Avoidance

International Journal of Printing, Packaging & Allied Sciences, Vol. 4, No. 1, December 2016 580
Abstract--- The Vehicular Ad hoc Network (VANET), gives most ideal chances to the prospective analytics in the
Intelligent Transport System (ITS) space, for example, secure passage of vehicles by keeping away from possible
threats out and about, pre-educated insights regarding road circumstances, close ahead blockage state of the
course, most excellent option ways in the direction of goal amid voyage, infotainment administrations, best business
administration’s from different administration suppliers, and so on. The Traffic Congestion (TC) is treated as
obstacles in the planned journey. The traffic-congestion avoidance event dissemination (T-CAED) algorithm has
been an occasion driven method for emergency event management, and road TCs are avoided with successor
vehicles with its operations. The ITS with VANET and even distance placement of road side unit(s)’ (RSU) on road
side assists intelligently to the vehicles in the journey. The study tries to assist the transporters, during TC, via the
two kinds of options to do T-CA. The T-CAED algorithm is based on prioritization calculation and is actualized with
the rundown of occasions and highlighted occasions. The TC identified intimation and T-CA options intimation have
been dispersed to the vehicles via RSUs through the VANET and the vehicles are provided with two options of T-CA.
With the simulation, vehicle’s congestion avoidance success rate (CASR) is presented on this work to enumerate the
importance of un interrupted cum supported travel from source to destination of vehicles.
Keywords--- VANET, ITS, RSU, Traffic Congestion Identification, Traffic-Congestion Avoidance Event
Dissemination Algorithm.
I. INTRODUCTION
The road environment (RE) traffic congestions are due to sudden population of vehicles on a particular location
on the road, without notification. Such uninformed events block/restrict other vehicles movement on the RE. The
individual transporters involved in such populated vehicles on the RE are having their own interests or by force to
halt and start their travel with vehicles [1]. These TCs are happening due to natural or human interrupted accidents
or incidents. In such scenario, the identification and provision of awareness warnings to RE vehicles, provides traffic
safety and continuity of travel among the vehicles on the road [2]. VANETs are being backbone network systems in
ITS that encourage data trade among movable vehicles (V2V) without the necessity of perpetual system (V2I)
foundation [3], which helps the transporters to make effective decision on their road selection to reach destination.
The VANET has been a conceivable answer for outline arrangements among vehicular network systems, which
could take care of travel blockage issues [1, 2]. The vehicles with V2V gadgets could fill the need of vehicles on RE
with devoted, cooperative ITS (C-ITS) equipped and dedicated short range communication (DSRC) [3,4]. During
TC, the populated vehicles’ intensity is nearly grounded, if they have been isolated from population. To bypass the
P. Sivaram, Assistant Professor, Dept. of CSE, SRC, SASTRA University, Kumbakonam, Tamilnadu, India. E-mail:ponsivs@src.sastra.edu
S. Senthilkumar, Dept. of CSE, University College of Engineering, Pattukottai, Tamilnadu, India. E-mail:senthilucepkt@gmail.com
Event Notification in VANET with Traffic
Congestion Detection and Congestion Avoidance
P. Sivaram and S. Senthilkumar
ISSN 2320-4387 | © EDITOR IJPPAS

vehicles from the TC, V2V communication is the one stop solution, it can take care of the issues of early warning
over an extensive territory, so as to permit vehicles to get backup ways to go with the help of hybrid-VANET-
enhanced transportation system [8]. In this system, the vehicles’ real time communications are enabled between
vehicle on-board units (OBU), VANET, RSUs, and road-traffic server. The algorithm [8], suffers with scalability as
a problem and generates the success rate for less populated vehicles. Our proposed work focusses on scalability
issues of this work, to improve the involvement of large scale of vehicles, in VANET to achieve optimal route
identification to reach destination or by relaxing on appropriate environment to overcome the frustrations of
transporters. The ultimate goal of proposed work is not to get stuck on TC, and make an effective travel towards
destination. For the continuity of travel of vehicles, this work is proposed with two kinds of classified services in
terms of options using V2I, I2V and V2V communications to the vehicles and are: (1) options to select alternate
optimum route to reach destination by avoiding TC, (2) options to select nearby restaurants to get refreshed, and the
resting points to overcome the tired and relax among transporters. The Fig. 1 (a) shows the VANET environment in
an URE and the Fig. 1(b) shows, T-CA administration with Vehicle-RSU-Vehicle (VRV) communication.
(a)
(b)
Figure 1: (a) Vehicular Ad Hoc Network (b) VRV Communication of Proposed System
The proposed work provides different advantages in wide range, with information handling and dissemination of
events to an occupied group of vehicles and RSUs. The RSU’s installation on RE are very expensive [3]. The V2I
communication relies on the energy and processing capabilities. The TC clog has been a difficult issue in express
highways and URE. The occurrence of TC may possible due to the following reasons: (1) by the trouble making
transporters, who drive the vehicles in unexpected manner and leave the RE with the unaligned movement; (2) due
to the natural disasters or by the weather condition on the RE; (3) by living being made accidents or incidents.
Because of these causes, the vehicles have been either stopping or passing with little pace on the RE. To lessen,
these clauses on RE during travel, novel ITS’s are being explored and are supporting the RE vehicles to reach their
destination without time delay [11]. VANETs empower all performing artists (vehicles, RSUs) in movement for the
purpose of trading data and to arrange their conduct. The TC can be reduced and be avoided with VANET based ITS
[12]. The nearby RSU of the TC area collects, the data from the mishap vehicles (location, speed, and course of
actions) and identifies the it with its non-preemptive based execution and spreads the message in the form of event
dissemination with successor vehicles, using other RSUs on RE.

II. RELATED WORKS
Y. Li et al. [1] represented, the predictability of vehicles halting time for large URE with the vehicle traceability.
With day/time basis vehicle traces, the vehicles’ population on URE is evaluated to predict the possibilities of
number of vehicles increase or decrease on RE, which is the key factor to control urban traffic vehicular congestion.
The halting time traces of vehicles provide the possibilities of predicting congestion on URE. The cooperative
perception of RE, and effective advisory warnings to the transporters, yields considerable active safety precautions
to avoid collision of vehicles on RE and is described by F. Naujoks et al. [2]. The DSRC technology and its
importance in VANET to enable safety and non-safety applications with optimal channel access and mobility- or
topology-aware algorithms are addressed by K. A. Hafeez et al [3]. L. Chen et al. [4] presented the concept of road
intersection awareness with transporters in the form of survey to handle vehicle collision warning and collision
avoidance during uncertainties on RE. J. H. Lim et al. [5] discussed about optimal strategy for safety messages
dissemination in VANET with V2V communication. An experimental analysis on vehicular networks using IEEE
802.11p standard is proposed by A. Fernando et al. [6]. The TCP based communication and its possibilities on
infrastructure based VANET are analysed by L. Ha et al. [7] and M. Wang et al. [8] presented a real-time path
planning method, during congestion on RE for vehicles. E. Egea-Lopez et al. [9] represented, the channel congestion
control methods with the FABRIC algorithm. N. Akhtar et al. [10] presented their work on topology for VANET
with vehicle mobility and communication channel models for realistic and simulation environments.
An event driven architecture with cooperative approach to detect road traffic congestion is proposed with the
research work of F. Terroso-Saenz et al. [11]. B. Zhang et al. [12] presented the need of placing road side access
point in uniform distance, and minimize the number of units necessary for VANET based vehicles support. The
security essentials for VANET transactions and with the basis of IoV is prescribed by W. Fu et al. [13]. The street
centric approach on routing protocol is discussed and its advantages are shown with results by X. M. Zhang et al.
[14]. M. A. Togou et al. [15] narrated and proved with results about stable content distribution system based routing
protocol for VANET and in which routing paths are selected with minimum end to end delay for non-safety
applications. For the effective RSUs’ placement on VANET, C. M. Silva et al. [16] presented, tradability of mobility
of vehicles in URE to enhance ease of V2I contact opportunities. M. Rohani et al. [17] represented, the importance
of vehicular positions in VANET applications, through global positioning system. X. Cao et al. [18] described in
their paper about the significance of minimizing the end to end delay of devices/resources allocation in VANET
applications among vehicles using V2V communication. L. W. Chen et al. [19] represented, a novel approach on
vehicles rear-end collisions avoidance in wireless sensor networks, which was based on BIG-CCA framework. P.
Sivaram et al. [20] presented the importance of the standard architecture in in-vehicle ECU’s network, and presented
an open system architecture called AUTOSAR. The importance of remote diagnostics support for the running
vehicles on the road with ITS and VANET support is presented using RTR framework, RDS protocol and ORiVD-
RDSS by P.Sivaram et al. [21]. The aim of our proposed work is to avoid TC, with the successor vehicles on RE. To
achieve this event notification is performed within RSUs and vehicles using VANET. The T-CAED algorithm is the
heart of the work and in addition, the TC detection also introduced. With these two activities, the vehicles CASR is
presented as result to enhance the importance of events notification in VANET. Both the actives are taking part in

the RSU by collecting necessary data items from the running vehicles’ OBU. These activities are real-time system
based activities, in which the time delay in event dissemination is not accepted from both V2I and I2V. The TC
identification and T-CA event dissemination in VANET, supports successor vehicles to avoid accidents/incidents
and TCs. This enhances the successor vehicles to make sophisticated, timely and cost effective travel.
III. PROPOSED SYSTEM
The proposed work consists system components as RSUs and vehicles in VANET. The nodes in this network are
vehicles and RSUs and the system is described as a communication system with optimal decision making support
for the vehicles. The communication methods in the system are three types and are V2I. I2V and V2V, where “V”
refers vehicles and “I” refers infrastructure as RSU. Then communication methods are renamed into V2R, R2V and
V2V. At present movement, many data communication frameworks have been concentrated in vehicular
applications by utilizing VANET components [22]. Such frameworks might, for instance, do not get together the
prerequisites of a blockage evasion functions, which involves emergency event dissemination, since they encompass
long postponements and might need expansive limit because of the huge geological zone of URE administration.
Conversely, VANET-oriented emergency event notification must have non-pre-emptive schedule on RSU’s
processors and this indicates the high priority level of event dissemination with nodes, in the proposed system.
Besides, the arrangement of RSU’s in VANET could be appropriated (uniform distance placement), that enhances
the stage pertaining with adaptability, reliability and confirmability. In recent years, the large-scale population of
vehicles on URE, introduces on running vehicles, the challenges to avoid traffic and continue the travel towards
destination. This challenge is mostly focusing on the vehicles dynamics on URE, and produces the complexity in
prediction of their movements. The existing traffic control systems are suffering in such scenarios, and the
optimized solutions are in need to provide services to overcome the traffic congestion related inefficiencies on RE.
A. The Environment
The event notification on proposed work is purely based on the purpose of congestion avoidance and provides
the continuity in travel to the successor vehicles on the RE. OBU: It is the vehicle’s unit, which communicates with
the RSU in VANET. It consists processor, memory, and data processing algorithms in it. GPS: A framework based
on satellite, which shows data to GPS beneficiaries on the position, empowering clients to decide on scopes and
possibilities. RSU: It is a wave gadget normally settled down the side of the road or in committed areas like in the
intersections, at Traffic flag, or close parking spots devoted for ranging, that uses short correspondence with 802.11p
radio communication, and being as a unit in VANET Infrastructure. Principle capacity of RSU is as under and
extend communication range of vehicles’ support, which assists in service provision on safety application,
emergency management assistance and remote diagnostics support etc. Based on vehicles movement, the congestion
in the RE is determined. V2V communication and V2I communications are major concerns in congestion avoidance.
The RSU gather the vehicle details like latitude, longitude and speed. Using VANET, we concentrate on two events:
one is alternative route provision and the second is the travel related service providers with the location details of
restaurant, refreshment centres, hospitals, theatres, petrol bunks etc. The vehicle met with incident or accident is
treated under emergency management on RE and is not a part of our work.

B. Traffic Congestion (TC) Identification
The VRV architecture of this communication system components are vehicles’ on-board units (OBUs) and
RSUs. The RSUs get environment data and compute activity for the specific area and transfer clog data to the
vehicles of all vehicles intended for continuing the travel. The vehicles and RSUs are communicating in regular
interval. The RSUs placement on road side is uniform in distance, where the RE running vehicles are covered within
their communication range. In this context, a vehicle leaves a RSU’s range, enters into another RSU’s
communication range. From the vehicle’s OBU, the speed, GPS location and direction of travel on road are
collected and sent to nearby RSU. The speed of the vehicle as a parameter, which is used to identify the TC. In the
proposed work, RSUs are prioritized with the activity called monitoring and collecting, the mentioned parameters
from the running vehicles on the RE. The Fig.2(a), represents the TC-identification algorithm. This RSU’s TC-
Identification algorithm, monitors the vehicles’ speed, GPS position, and direction of travel to determine the
mobility of vehicles are proper or they are interrupted on RE. The speed value (0 < 20) km/hr is taken as threshold
to determine TC. If the one or two vehicles are halting on the side of the road, the RSU justifies with the parking
location of vehicle with GPS position and verifies the other successor vehicles speed. If the current vehicle’s road
side parking or the successor vehicles speed on the same direction, improper or falls down to the threshold and the
successor vehicles halting is increasing nearby the current vehicle, RSU identifies the TC. The TC is immediately
reported to adjacent RSUs, according to the RE as express way or not. The successor/predecessor RSU’s range of
vehicles are passed with an emergency event to take alternate options to continue their travel.
text
(b) T-CAED Algorithm
Prior State: Estimate the time needed to clear TC and assign an Unique Id for it.
Function T-CAED (UID, GPV, APT, OST, T) returns an event to avoid CA
Inputs: UID, an unique ID of the current TC.
GPV, GPS positions of successor vehicles on RE.
APT, an alternate path table, a predetermined shortest routes to avoid CA.
OST, other travel related services table, details of other services provider.
T, time interval in seconds, TC clearance time interval.
Repeat until No vehicles on RSU’s range and with in the T seconds.
Step 1: Send TC event notification to successor vehicles with RSU’s vehicle
table.
Step 2: Append 2 options with successive event notification to same
vehicles, mentioning choices to current T-CA.
Step 3: A successor vehicle selected option 1: To travel in alternate path
if (successor vehicle accepted option 1)
{Find the direction of travel on RE.
From APT list 3 alternate paths based on its GPS position
to T-CA.
Update the RSU’s T-CAED table.}
Step 4: A successor vehicle, selected option 2: to utilize services related
to relax.
if (successor vehicle accepted option 2)
{Find the direction of travel on RE.
From OST list 3 resting service points, before the current
TC area.
Update the RSU’s T-CAED table.
Send an event notification to the service point, which is
elected}
End
Post State: RSU update its status table of CA of vehicles’ options and monitors for TC.
(a) TC-Identification Algorithm
Prior State: The running vehicles and fixed RSUs communicates each other to identify TC on RE.
Function TC-Identification (V_SP, GPS_P, DT) returns TC and repeats the function
inputs: V_SP, vehicle speed, in Km/hr.
GPS_P, the GPS position of the halted vehicles on the RE.
DT, direction of the travel of vehicle on the RE.
Repeat Steps 1 – 4, until No vehicles on RSU’s range.
Step 1: Identify the vehicles, below 20 Km/hr.
Step 2: Find and monitor the vehicles reaching 0 Km/hr.
Step 3: Identifying TC.
if (successor vehicles speed is < 20 Km/hr)
{Check the first halted vehicle’s GPS position
Identify angle of halt of the vehicle on the road
if (GPS_P is on the DT, perfect and normal)
{Normal-Perfect halt–TC clearance with min time
Move to Step 4}
else if (GPS_P angled and not on the DT and improper halt)
{Imperfect halt–TC clearance with extended time}
Move to Step 4}
}
Step 4: Report the vehicles halt as TC to nearby RSUs.
if (the road is not express way RE)
{Report TC with successor and predecessor RSUs
of current RSU}
else
{Report TC with successor RSUs of the current RSU}
End
Post State: TC event notice is received by successor and predecessor vehicles on the RE.
text
Figure 2: (a) TC-Identification Algorithm (b) T-CAED Algorithm

C. Traffic-Congestion Avoidance Event Dissemination (T-CAED) Algorithm
The clog data has been moved as an event from RSU, to the vehicle's advanced OBU, which would be drawing
the picture of the specific congested area. The blockage data has additionally transferred to the Road Side Units
situated in the close-by activity signs. This data could be utilized by the transporters, as well they could take
alternate options to select and continue their travel. Immediate after the TC detection, the T-CAED algorithm takes
control over the environment and runs on RSU to generate and disseminate the TC event notification to successor
vehicles on the RE. The Fig. 2(b), represents, the T-CAED algorithm. The algorithm begins with assigning a unique
ID for the TC and in addition, it estimates the approximate time needed in the TC clearance. This takes input
parameters for processing as a unique ID of the current TC, GPS positions of successive vehicles on the RE (nearer
to the TC area), pre-programmed available alternative route information table entries, pre-determined available other
travel related services table entries, and time interval of TC clearance. After the TC detection, the successors are
notified with the first event as TC occurrence detail and in consequent mode, second event is appended with time
interval of TC clearance with alternate options for continuing travel.
Option 1: The nearby road diversion from the main route of travel, which leads to avoid TC and continue the
travel by without compromising on travelling time and cost.
Option 2: The nearby locations of relaxation related service providers on the road side for travellers. The services
are namely restaurant, refreshment centres, hospitals, theatres, shopping malls and hotels etc. According to the time
slot of congestion occupation on the RE, the travellers are provided with such options to overcome their relaxation
based activities and continue their travel. Through such options provision, the transporters are free from wastage of
time during their travel towards destination. The V2I and I2V is the communication between vehicle and RSU,
which facilitates the TC detection and T-CA with event dissemination. The identified TC details are passed to the
successor vehicles on the RE and vehicles’ responses as selected option is collected as messages at RSU.
IV. PERFORMANCE ANALYSIS
The vehicle’s speed is a major concern in this communication system to detect TC. The congestion density is
determined through number of vehicles halted in the TC area. Due to uninformed mishap (accident/incident) on a
specific vehicle, the successor vehicles are forced to find the availability of the road to continue their travel in safer
and continuing mode. If the availability of the road is lagging to continue the travel, the immediate successors are
supposed to be halted nearby the mishap vehicle. In such scenario, the traffic gets congested and TC occurs. To
conduct the performance analysis, we did a simulation with the Simulation Urban Mobility (SUMO) tool and
analysed the significance between the speed of the vehicles and the location of the vehicles; and the location of the
vehicles and the time of travel of the vehicles. If the TC is occurred, the vehicles are halted nearby a specific point
on the road, and the blockage on the RE is introduced. The lack of road availability initiates other successor vehicles
on the road to stop at that specific point and generates the population of vehicles and is called traffic congestion. The
vehicles inflow per hour on the URE is provided with 1050 vehicles, and this projects the URE with large scale of
vehicles populated. The maximum speed of a vehicle is provided with 108 km/h, and the simulation time interval is
one hour. An event as a message, its size is 1000 bytes, where the event dissemination happens between vehicle to

RSU. The vehicles parameters speed, GPS position collections are rest with RSU monitoring and its range of
vehicles are monitored. The events are disseminated after the identification of TC, i.e., the first event is from RSU to
its range of vehicles and nearby RSUs, by indicating the details about the TC, such as area of TC on road, distance
from the vehicle’s current location, and the time to reach if the vehicle travels on the specific speed. The second
event is a message, which includes the options 1 and 2 earlier discussed, in spite of T-CA. The Table 1, presents the
simulation parameters.
Table 1: Simulation Setup Parameters
Simulation Parameters Values Simulation Parameters Values
Time Wrap 10 times MAC IEEE802.15.4
In Flow 1050 vehicles/hour RF output power/receiver sensitivity -4dBm/-84dBm
Maximum Speed 108 km/h Sim time in seconds 3600 seconds
Lanes 3 lanes Transmitter range 20 km
Antenna type Omni directional Bandwidth 2 MB
Mobility model Random way point model Packet size 1000 bytes
Network area 40 X 60 Buffer Length 50 packets
Traffic rate / type (Node / min) CBR / 1 Propagation model Two-way ground
The Table. 2, describes the RSU assigned vehicle IDs with their speed, GPS based distance from the origin
(DFO (TC area)) to the successor vehicles on the road, position of the vehicle on the road, and status. The vehicle ID
is assigned based on the current RSU’s ID and its range of vehicles. The RIDCD represents the combinations of
RSU’s ID, TC-identification and the numerical value represents the vehicle’s identity on the current production
cycle of RSU. The speed of the vehicle, which is measured in km/h, and is collected by considering a mishap vehicle
area and the successors are gradually reducing their speed towards halting and this is uninformed traffic blockage on
the road to the immediate successors.
Table 2: Traffic Congestion Detection with Simulation Environment Extracted Values
Vehicles Speed (km/h) DFO (Km) Position on RE Vehicle Status
RIDCD436 0 0 Improper Mishap Halt
RIDCD437 10 0.013 Proper Halted on TC
RIDCD441 65 9.9 Proper On the RUN
Table 3: GPS based RSU’s Vehicle Tracking Details to Identify TC

The Table 3 represents the GPS based RSU’s vehicle tracking details to identify the TC. The RSU’s, TC-
identification algorithm identifies the TC with vehicle’s speed monitoring and confirms the TC. Based on the type of
the mishap on the committed vehicle, the RSU further estimates the time taken to recover from the TC and is
responded to the successor vehicles on the RE with first event notification. The T-CAED algorithm manipulates the
second event with two options and disseminates to successor vehicles. To make decisions on T-CA, the RSU
provides two options in its communication range of vehicles with the second event notification. They are
categorized into option 1 and 2. The option 1 is the alternative route (AR) provision to bypass TC area and catch the
same route of travel with shortest path and is estimated based on shortest path first and is even categorized with
paved and unpaved road conditions. The option 2 is the transporters’ relaxation related business model. The vehicle
may take different options based on transporters interest. If the vehicle subscribes the alternative path,
it will consider the unpaved and paved routes and its distance to reach the destination. Based on the distance and the
vehicle will select the shortest path and short time interval. Some vehicles may subscribe to utilize the refreshment
centres or hotels for their congestion avoidance. Based on the vehicles count the congestion will be determined.
Table 4: Paved and Unpaved Alternate Routes Options on Second Event’s Option 1
Vehicle/Node
Paved AR
Distance (km)
Paved AR Time of
Travel (min)
Unpaved AR
Distance (km)
Unpaved AR Time of
Travel (min)
RIDCD441 15 15 14 20
RIDCD442 22 25 24 34
RIDCD443 19 20 16 19
RIDCD444 23 32 21 25
The Table 4 shows paved and unpaved ARs selection by the vehicles to bypass congestion. Based on the road
condition with distance and the time taken to reach the destination are considered by the successor vehicles. If the
road is unpaved in AR, the vehicles are taking more time to reach the destination. If the road is paved the vehicles
take minimum time to reach the destination. Moreover, irrespective of road condition, the bypass time interval
suggestions from the RSU’s are major key factors, with which, the transporters are relaying on to make use of their
available time to spend effectively to reach destination. With the results, from table 2, consider the vehicle record
RIDCD441, in which, the paved AR distance is 15 km and for unpaved AR is 14 km. The distance to bypass TC on
a specific area on road is very less in unpaved AR comparing to paved AR selection for the transporter to lead his
vehicle towards destination. In the same time, the time taken to bypass the TC using paved AR selection is 15
minutes, and unpaved AR selection is 20 minutes with the specific consistent speed of the vehicle.

Figure 3: (a) Population of Vehicles-time, (b) Population of Vehicles-location on the Road, (c) Population of
Vehicles-time, Location on the Road
0
10
20
30
40
50
60
70
0
20
40
60
80
100
120
140
160
1 2 3 4 5 6 7 8 9 10 11 12 13
TimeInterval(mins)
Populationof
Vehicles
Sections
Vehicleswith Option 1, 2 andwithout Options
RSU Rangeof Vehicles (Nos.) Vehicles Opted Option-1 (Nos.)
Vehicles without Options 1 & 2 (Nos.) Vehicles Opted Option-2 (Nos.)
(a)
(b)
Figure 6: (a) RSU Range of Vehicles and CASR on TC (b) Vehicles Opted Options 1, 2 and Without Options
This gives the transporters an opportunity, whether to relay on distance based factor or time interval based factor.
In such scenario, the time is the factor, when the transporters decided to continue their travel and most of the
transporters will rely on the TC bypass time interval based AR selection. This shows, the T-CAED algorithm from

RSUs which yields more accurate predictions to the transporters to bypass their vehicles to reach destination. This is
based on two factors and are exact distance to bypass TC, and the approximate time to bypass TC. As for the option
2, the details of nearby services units with relaxation services for transporters, based on the vehicle’s location. The
transporters can select the services based on their interest and the transporters are avoiding the TC. The large scale
of vehicle handling capability of this proposed system is demonstrated with the simulation values as the number of
vehicles per hour in URE is 1050 in numbers. The time interval of the simulation is provided as 60 minutes and the
distance of the road taken as 260 km. The Fig. 3(a) represents, the population of vehicles with respect to the time
interval, which is the range of multiples of five in minutes and it is very clear that the population of vehicles are
increasing with respect to the time increase. The Fig. 3(b) presents, the population of vehicles with respect to the
location positions in URE. The Fig. 3(c) the combinational analysis on time, location and population of vehicles are
presented. The successor vehicles acceptance on the congestion avoidance is presented in the form of CASR.
Where, CASR is congestion avoidance success rate; VAPO is the alternate path opted vehicles; VRPO is the
relaxation related services opted vehicles; and VTOT is the total number of vehicles on the range of TC event
dissemination.
The Table 5, provides the data collection on simulated URE of a single RSU’s performance on T-CA. By
considering the time duration during 12.30 pm to 1.30 pm, the simulation actions are performed. The time interval
now has the new dimension as multiples of 5 mins from 12.30 pm to 1.30 pm. These tabulated data collection
presents the importance of CASR. The Fig. 4(a) is the population of vehicles within the range of a single RSU
(which is equipped with T-CAED algorithm) with the CASR during TC. In this test case, every five minutes are
assumed as occurrence of TC, and based on publish/service frame work, vehicle responses on option 1, 2 and
without options elected are recorded to show the importance of CASR. The Fig.4(b) presents the elected options of
vehicles during 60 minutes’ duration.
Table 5: Simulation Results by Assuming Every Five Minutes, a TC occurs in URE
RSU Range
of Vehicles
(Nos.)
RSU Time
Interval
(mins)
Vehicles
Opted Option-1
(Nos.)
Vehicles
Opted Option-2
(Nos.)
Vehicles without
Options 1 & 2
(Nos.)
CASR
36 0 17 15 4 88.89
40 5 28 10 2 95.00
46 10 29 16 1 97.83
66 15 25 41 0 100.00
75 20 10 60 5 93.33
76 25 12 58 6 92.11
77 30 15 60 2 97.40
83 35 71 5 7 91.57
84 40 55 27 2 97.62
98 45 56 42 0 100.00
99 50 77 20 2 97.98
120 55 88 29 3 97.50
150 60 129 20 1 99.33

V. CONCLUSION AND FUTURE WORK
To manage large scale vehicles in this communication environment, the processing capabilities of RSUs and
OBUs must be enhanced. The TC identification is a continuous process on RSU, which happens until no vehicles on
the communication range of RSU and requires high speed processors in it. Through the simulation analysis, it is
very essential to equip RSUs with high speed processors to monitor and manage TC detection and T-CA processes.
A characterization of congested RE and normal RE, measure must be acquired, to feed the RSU, to intimate the
vehicles about the TC. The CASR shows the importance of this system on large scale of vehicle in URE, and the
transporters are free from frustration on travel. As a further enhancement of the proposed work, the reduction or
eliminating the role of RSUs in VANET, to disseminate events. This cuts the cost spent on installing and
maintaining the fixed RSUs on road sides.
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