Introduction to intelligent transportation system: overview, classification based on physical architecture, and challenges

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Int. J. Sensor Networks, Vol. 38, No. 4, 2022 215
Copyright © 2022 Inderscience Enterprises Ltd.
Introduction to intelligent transportation system:
overview, classification based on physical
architecture, and challenges
Shivani Sharma* and Sateesh Kumar Awasthi
Department of Electronics and Communication Engineering,
Dr B R Ambedkar National Institute of Technology,
Jalandhar, Punjab, 144001, India
Email: shivanis.ec.19@nitj.ac.in
Email: awasthisk@nitj.ac.in
*Corresponding author
Abstract: The rapid increase in the number of vehicles has become a major problem associated
with traffic management. To ameliorate the traffic efficiency and minimisation of mess, the
traffic management system (TMS) comes into existence. Traffic control centre (TCC), intelligent
transportation system (ITS), and CCTVs are the major elements of TMS. ITS is considered a key
element of TMS. It is known for the management of traffic mobility, and management of
vehicles by assisting the drivers. ITS is responsible for establishing the communication between
the infrastructure and vehicles or among the vehicles. Physical ITS architecture has significance
in establishing the link between physical entities and data flow. The current article focuses on the
introduction and classification of the ITS model such as centralised, distributed, and hybrid based
on the physical architecture. Special emphasis has been given to discussing the issues linked with
these models during data dissemination and rerouting.
Keywords: intelligent transportation system; ITS; VANETs; DSRC; IEEE 802.11p; broadcast
storm; communication overhead; traffic management system; TMS; traffic control centre; TCC.
Reference to this paper should be made as follows: Sharma, S. and Awasthi, S.K. (2022)
‘Introduction to intelligent transportation system: overview, classification based on physical
architecture, and challenges’, Int. J. Sensor Networks, Vol. 38, No. 4, pp.215–240.
Biographical notes: Shivani Sharma is pursuing her PhD in the Department of Electronics and
Communication Engineering, Dr B R Ambedkar National Institute of Technology, Jalandhar. She
received MTech and BE n Electronics and Communication Engineering from SLIET Longowal
in 2016 and 2019 respectively. Her research interest includes low power VLSI circuit design and
VANETS.
Sateesh Kumar Awasthi is an Assistant Professor in the Department of Electronics and
Communication Engineering, Dr B R Ambedkar National Institute of Technology, Jalandhar. He
received his PhD from IIT Kanpur. His research interest includes peer-to-peer networks, wireless
sensor networks, complex networks, social networks, solution of non-linear equations,
application of linear algebra, and game theory in networks.
1 Introduction
Faster mobility is the basic need of modern society. Most of
the population relies on autonomous vehicles. In urban
areas, due to continuous population growth, the number of
vehicles is abruptly increasing as compared to the available
infrastructure. This leads to congestion, that further results
in a negative impact on the environment and society in
terms of traffic accidents, the decline in economic growth,
and abrupt greenhouse emission. There is a requirement for
a mechanism in vehicles that can sense the weather
conditions and congestion around them. Thus, can intimate
their drivers about the change in weather, potential risks,
and traffic flow in a certain region (De Souza et al., 2017a;
Korablev et al., 2021). For the perfect traffic flow
management and control purpose, real-time data in the
terms of traffic flow, speed of vehicles, the density of
vehicles, headway between the vehicles are required
(Korablev et al., 2021). Currently, the most renowned
technology serving the above purpose is known as
intelligent transportation system (ITS), which is almost the
collective distribution of all engineering disciplines and is
used to provide safe, well-organised, and durable
transportation. ITS can be understood from two different
basic concepts.

216 S. Sharma and S.K. Awasthi
One way is to consider ITS as a data-concentrated
application, where the collected data are processed and
disseminated with the help of an associated network of
smart devices, communication, and transportation
infrastructure. In this case, ITS as a whole system is divided
into three parts namely:
1 the data generation and processing phase
2 the data recording/storing phase
3 the smart decision-support devices.
ITS applications are very complex and have data-intensive
features that can be explained with the help of ‘5V’s of
large data’. These 5 V’s include volume, variety, velocity,
veracity, and value (Khan et al., 2017). The first ‘V’ may be
defined as the volume of traffic data which is increased
exponentially in past few years. The second ‘V’ is the
variety of collected data in different formats from different
sources. This data can be in the form of video, image,
numeric value, and text collected by CCTV, OSM, sensor,
and social media respectively. Data can be organised either
in a structured or semi-structured format. Structured data is
in the form of numeric data whereas semi-structured data is
in the format of images, videos, and audio files. Velocity is
considered the third ‘V’ of ITS. The collected ITS data can
be observed continuously in real-time data and can be
collected in regular intervals. Data rates and processing
requirement changes drastically from historic data
processing to real-time incident processing of data extracted
from online mode consequently leading to the high demand
for data infrastructure. Veracity is the fourth V that comes
under this category. Veracity defines the trustworthiness of
the data. For this purpose, sensors must be calibrated so that
they can provide an accurate data stream without any
missing information. The collection of efficient and timely
data is still a big issue in the ITS field. The final ‘V’ is the
value that is based on the age of collected data, sampling
rate, and also on the ITS application. For data collision the
data collected that was collected past a few minutes ago is
useless but at the same time, it is useful for the route
planning application.
A second way to realise the ITS is considering a layered
architecture similar to the OSI reference model. The
primary layer of this system contains all physical
transportation components, laptops/personal computers, and
restoring devices. The next layer is the data link layer that is
responsible for defining the communication protocols for
the network technologies, either wired or wireless. Here
internet protocol (IP) is the protocol for the establishment of
connection among different network technologies. It is
responsible for vehicular communication through
smartphones with the data centre. Above this, there is a
transport layer in the count, protocols of this layer ensure
the process-to-process reliability of the communication even
in the dynamic topology. The session, presentation, and
application layers are merged herein single layer which is
responsible for data formatting expected by a specific
application, and information management among users and
among users and system.
ITS was originated in 1996 in countries like Europe and
the USA. The basic motivation behind this approach came
from the situations like the traffic jams that occurred in
China and Russia which took over 12 and 7 days
respectively to clear the jam (An et al., 2011).
The ITS utilises the best use of available infrastructure
for safe traveling. Roads are defined as the basic key
parameters of infrastructure when we define mobility,
convenience, and economic development specifically for
tourism (Li et al., 2020). The ITS is an integrated system
that connects roads, commuters, and vehicles appropriately.
Its goal is not only to check on traffic issues but also to take
care of socio-economic matters (Rakha and
Kamalanathsharma, 2011; Doolan and Muntean, 2017). ITS
makes use of moving vehicles with embedded sensing
elements, on-board unit (OBU) and roadside unit (RSU) as
infrastructure units. OBU captures the information from
different types of sensors. It is responsible for the
management and computation of information at high speed.
It makes inter-vehicle communication possible by
displaying warnings and issuing mandatory alerts. OBUs
are generally expensive items, thus everyone cannot afford
to inbuilt these in his vehicle. Smartphones with inbuilt
sensors can be used as OBU emulators. RSUs act as a
gateway unit between OBUs and communication
infrastructure, they are placed aside the roads. They use
dedicated short-range communication (DSRC) to provide
connectivity to the moving vehicles.
With the help of moving vehicles, ITS provides
complete information regarding the road congestion in any
particular area rather than placing the building and tower in
any particular area. ITS manages to resolve key issues at
three different levels: community, infrastructure
administrator, and commuters. On a community basis, the
application of ITS would reduce road accidents and
congestions based on real-time traffic data (Araujo et al.,
2014; Meneguette et al., 2015; De Souza et al., 2015b). At
the level of infrastructure administrator, the affected areas,
i.e., accident-prone and congested areas can be easily
identified and maintained by the ITS at a lower cost and
efficient ways (Rakha and Kamalanathsharma, 2011; Pan
et al., 2015; De Souza et al., 2016; Rizzo et al., 2011). By
identifying the appropriate road, commuters would be able
to take the advantage of the economy and can get rid of
more toll payments (Doolan and Muntean, 2017). Some
countries like the USA, Japan, and Korea have tried to
implement the ITS in various regions and communities
experienced positive impacts (De Souza et al., 2017a;
Korablev et al., 2021). However, they are facing some
issues in its implementation like the integration of
heterogeneous data from various sources, implementation
cost, unavailability of expertise in this area, and also the
dissemination problem. Variable speed and topology (Zhang
et al., 2011), non-uniform distribution of traffic (Shen et al.,

Introduction to intelligent transportation system 217
2014), bandwidth limitation (Bouassida, 2011), and
decentralisation (Tangade and Manvi, 2013) are the basic
characteristics of ITS that make it inefficient in
performance. These are the issues responsible for the
occurrence of traffic congestion, and collision.
Collectively, the traffic management system (TMS)
initially collects real-time data from different kinds of
sources like CCTV, traffic lights, and inductive loops. After
that this traffic data gets aggregated and exploited in a
cooperative way or a traffic management centre (TMC).
This is for the identification of any unusual incident, and to
take further actions to control the hazards. Consequently, it
results in a smooth flow and hence will increase the traffic
efficiency. An ITS is the essential block of TMS, which is
responsible for the management of traffic mobility. ITS
have a vast range of applications (Baiocchi et al., 2015).
Majorly ITS applications are grouped under six categories
namely:
1 Advanced public transportation system (APTS):
responsible to provide information to the public
transportation commuters so that they can make
appropriate decisions regarding the system and
operation.
2 Advanced traveller information system (ATIS): provides
data directly to travellers to select the routes, traveling
time, and travel modes3.
3 Advanced traffic management system (ATMS):
responsible for monitoring the traffic flow so that
commuter can make the decision promptly.
4 Automated highway system: responsible for automatic
controlling of vehicle’s steering, brakes, and throttle.
5 Incident management system: provides variable
message signs to warn the commuter regarding the
unusual condition include accident and other delays.
6 Commercial vehicle control (CVC): responsible for
border crossing, fleet management, and control loss
warning.
Vehicular ad-hoc network (VANET) is the key feature of
the ITS framework, sometimes it is also known as an
intelligent transportation network (Al-Sultan et al., 2014).
It is used for the data exchange among vehicles, between
vehicles and infrastructure, and TMC. Vehicles are the
mobile nodes in VANETs with the OBUs embedded with
processors, sensors, and wireless units that can
communicate among themselves to create the ad-hoc
environment. VANETs use DSRC for establishing
communication among vehicles, and vehicles and RSU.
Whereas they use long-term evolution (LTE)
communication for the data exchange between the RSUs
and central server (Lu et al., 2014). For traffic congestion
and avoidance, various authors have proposed the solution
by using three basic models of ITS, i.e., centralised,
distributed, and hybrid.
Mainly by considering the traffic congestion as the key
issue, several ITS models have been suggested in past, in
which the solution is given in terms of adjustment of
vehicles speed to reduce waiting time at the traffic light
(Rakha and Kamalanathsharma, 2011; Doolan and
Muntean, 2017; Meneguette et all., 2015), creating headway
among vehicles to reduce the collision (De Souza et al.,
2015b; Bauza and Gozalvez, 2012) and provide an alternate
route to reduce the congestion (Pan et al., 2012, 2016; Wang
et al., 2016; De Souza et al., 2019).
Laconically, as road traffic is a daily affair, researchers
from divergent fields have been captivated to develop ITS
to distribute in this concern. However, ITS development is
still facing challenges. In this manner, this paper primarily
focuses on introducing a survey that can give detailed
knowledge to the researchers for apprehending the main
rudiments and challenges associated with ITS, covering
various topics from communication to its applications.
Therefore, the major beneficiation of this paper includes:
1 an overview of the development of ITS and the
technologies used by it in various countries
2 detailed classification, review, comparative study of
various ITS models based on the physical architecture
in terms of data dissemination, rerouting, coverage
area, and network overload
3 key challenges in ITS development in the present
scenario.
As per our knowledge, nobody in past had given the
detailed ITS model comparison in terms of physical
architecture used, having the goal of congestion detection
and avoidance.
Figure 1 Overview of the paper (see online version for colours)
The rest of the paper is organised in the following fashion,
Section 2 covers the development of ITS and the key
technologies for it. Section 3 gives the introduction to the
basic architecture of TMS, ITS, and VANET. Section 4 will
cover the classification of ITS. Section 5 gives the
qualitative analysis of ITS models. Section 6 covers the key
challenges in the development of ITS and finally Section 7,
concludes the paper. Figure 1 shows the complete overview
of all the sections explained in the paper.

218 S. Sharma and S.K. Awasthi
2 ITS development and key technologies for ITS
This section gives information regarding the development of
ITS system in various countries and the technologies used
by these countries.
2.1 Development of ITS
The development of ITS in different countries across the
globe can be split into two parts. In the first part, the
characteristics were data acquisition and processing.
Whereas the second stage was all about the development of
technologies from the vehicle safety point of view, i.e.,
collision detection and its avoidance. In its initial days in the
1970s, transportation telematics was originated in Japan to
deal with various traffic-related issues like congestion and
road accidents. In Europe, PROMETHEUS was the first
formal transportation program started in 1986 that came into
existence in 1989. Further, in 1988 program named DRIVE
was developed. In 1990, the USA established the IVHS
program that came into existence in 1992. In 1994, IVHS
was renamed ITS. Smart transportation architectures for the
countries were designed in the first stage. In the second
stage, in 1986 DEMO and PATH were developed by
America for the establishment of an automated highway
system. European Union has developed an eSafety program
for autonomous driving and safety. In 2010 VisLAB was
launched by China. Table1 shows the historical
development of ITS technologies in several countries.
2.2 Key technologies for ITS
ITS varies in terms of applied technologies. Category of the
basic management system, i.e., traffic signal control, car
navigation, and speed cameras use different technologies,
whereas monitoring systems like, CCTV cameras use
different ITS technologies. Other applications that integrate
the current data and past data as feedback, mostly used in
parking guidance applications use some different
technologies. This section gives the knowledge of all kinds
of available technologies used in ITS.
Technologies for data collection and dissemination for
ITS applications are majorly divided into four categories,
namely:
1 infrastructure-based
2 vehicle-based
3 commuter-based
4 geographical-area-based.
2.2.1 Infrastructure-based data
It includes the information of traffic at fixed locations along
the highway. Sensors at roads are placed to gather the data
without interrupting the vehicle’s movement. Inductive loop
detectors, microwave radars, ultrasonic sensors, and CCTV
cameras are the basic technology used to collect
infrastructure-based data. This data is useful for the
applications such as real-time traffic monitoring, incident
verification and management, and vehicle classification.
2.2.1.1 Inductive loop detector:
It is an electromagnetic detection device that employs a
movable magnet to induce a current in the nearby wire.
Induction loop detectors in ITS are used as vehicle presence
indicators. An inductive loop acts as a tuned circuit having
loop wire and lead-in wire as inductive elements. When any
vehicle passes the loop or stopped within the inductive loop,
the ferrous material consequently increases the inductance
of the loop. Majorly the parking system uses the inductive
loop detector to track the occupancy in and out.
2.2.1.2 CCTV cameras
It is considered one of the most popularly used videos
vehicle detection techniques. It acts as an image processor
having a microprocessor-based CPU and the software that
computes the video images. Users can place virtual
detectors on the images that are being displayed on the
monitor with the help of a mouse and various interactive
graphics. This data is sent to the server for real-time traffic
analysis.
Table 1 Development of ITS
ITS Year Technology Country
Electronic route guidance system (EGRS) 1970 DSRC-5.9GHz and GPS USA
Integrated surface transportation efficiency program (ISTEA) 1991 DSRC-5.9GHz and GPS USA
Transportation equity act for 21st century (TEA) 1997 DSRC-5.9GHz and GPS USA
Vehicle information communication (VICS) 1990 DSRC-5.9GHz and Radio
wave or infrared beacons Japan
European road transport telemetric implementation coordination
organisation (EUREKA) 1985 DSRC-5.9GHz and GPS European Union (UK
France and Germany)
Programmed for European traffic with highest efficiency and unprecedent
safety (PROMETHEUS) 1987 DSRC-5.9GHz and GPS European Union (UK
France and Germany)
Dedicated short range infrastructure for vehicle safety in Europe (DRIVE) 1988 DSRC-5.9GHz and GPS Europe
National ITS master plan for 21st Century 2000 WiBro based on WiMAX South Korea

Introduction to intelligent transportation system 219
2.2.1.3 Sensors
Vision-based sensors, laser-based sensors, and ultrasonic
sensors are broadly used sensors. Vision-based sensors
make use of images that are captured by the camera to
analyse the presence, occupancy of the vehicles on the
roadway. Multipoint inspection can be achieved by a single
sensor. But its working gets affected during non-ideal
weather and light conditions. The laser sensor is another
technology that depicts the vehicle with its narrow
beamwidth. But its performance is rain and mist dependent.
The ultrasonic sensor is another sensor that falls under this
category it is used to measure the distance of the target
vehicle by sending the ultrasonic sound waves using
piezoelectric crystal and then converting the reflected sound
waves into electrical signals using the receiver. These
sensors are primarily used as proximity sensors. Automatic
parking and anti-collision system are the applications of ITS
that majorly make use of ultrasonic sensors.
2.2.1.4 Microwave radar
It is used to detect the vehicle’s speed on the roadway.
Continuous-wave radar placed above the lanes transmits the
electromagnetic signal with constant frequency. By doppler
principle, it will calculate the vehicle’s speed which is
directly proportional to the transmitted signal and received
signal. The change in frequency tells about the passage of
vehicles within the radar coverage range. But it is not an
appropriate approach to detect stationary vehicles. To
overcome the issue of continuous wave radar there is
frequency modulated continuous radar. It transmits the
sawtooth signal with varying frequencies concerning time.
It allows the detection of stationary vehicles by calculating
the time taken by the vehicle to travel within the
two-interval marks. The speed of a vehicle can be calculated
by the ratio of the distance between the markers to the time
taken by the vehicle to cover that distance. Also, this
detector can sense the stationary vehicle within the markers.
Their performance is restricted in some areas like the
underside of the bridge.
2.2.2 Vehicle-based data collection technology
This is the second source used in the ITS applications.
Vehicles with inbuilt electronic toll tags and GPS
configured with smartphones create the ad-hoc network
with highly dynamic topology and shares information that
includes location and speed with neighbouring vehicles
using DSRC protocol. This data is useful for the ITS
applications that are dedicated to route selection, origin-
destination preview, and travel time calculation.
2.2.2.1 Global positioning system (GPS)
GPS is a navigation system. It was initially intended for
military purposes by the USA Department of Defence, but
later in the 1980s, this system was available for civilian use
too. Previously, GPS was consisting of 24 satellites but
from March 2008 this count increases to 32 to improve the
precision of the GPS receiver. At present in 2021, there are
31 operational satellites in the GPS constellation
(GPS.gov, 2021). GPS receiver uses the signal of a
minimum of 3 satellites for the calculation of position in
2D, whereas it uses four or more satellites that continuously
transmit the signal to detect the location of the vehicle in
3D. GPS can provide real-time information regarding a
vehicle’s location and speed. GPS receivers give almost
accurate results up to 10–20 m. It can collect and store a big
amount of desired useful information. By installing the GPS
receiver in the vehicles as OBU, it is easy to get signals
from various kinds of satellites, and this communication
requires line of sight with the satellite. The canyon effect
restricts the use of this technology (An et al., 20100). GPS
is a very important technique used in various applications
such as automatic vehicle location, travel time and delay,
dynamic route guidance, advanced traveller’s information
systems. GPS is a battery-hungry device. Various countries
like the USA, Germany, and Holland are using the OBU
with an inbuilt GPS for all kinds of monitoring (Ezell,
2010).
2.2.2.2 Wireless network
Wireless networks are attainable when communication
among vehicles and Roadside infrastructure is required. But
it has a limited range of 100s of meters. Its range can only
be extendable by installing the relaying nodes between
vehicles and RSU. WiBro is the technology that South
Korea is dependent on, which itself is based on WiMAX
technology (Ezell, 2010).
2.2.2.3 Radio wave beacons
Radio wave beacons are used by the vehicle information
communications system (VICS) ITS system developed in
Japan at the express ways. Whereas infrared beacons are
used at the roads for the transmission of real-time traffic-
related information. VICS uses the DSRC technology at the
arterial areas which are quite dense as compared to other
areas as it connects towns with the cities.
2.2.2.4 Mobile telephony
3G and 4G/LTE standards of mobile networks are used for
data transmission in the various applications of the ITS. The
main advantage of these networks is their availability
everywhere e.g., in towns, cities, along highways. The basic
issue with this technology is the installation of base stations
in the less dense area that becomes an issue cost-wise for an
operator. This technology is not suited for the applications
from the safety point of view as it is very slow (Ezell,
2010).
2.2.2.5 Dedicated short range communication
Dedicated-short range communication (DSRC) is a simplex
or duplex wireless communication channel operating at

220 S. Sharma and S.K. Awasthi
5.8 GHz spectrum (i.e., from 172–184 GHz). This is
specially designed for automotive purposes. DSRC uses
IEEE 802.11p, nowadays collectively known as wireless
access for vehicular network (WAVE) (Arena et al., 2020).
Figure 2 shows the DSRC-WAVE architecture. Architecture
is divided into two basic parts:
1 IEEE802.11p for physical and MAC layer
2 1,609 for the safety and network management
applications. DSRC has 7 licensed channels (6 service
channels and 1 control channel).
It is used by ITS applications for establishing the
communication between vehicles and RSU, among vehicles
going on the road. It uses the 5.8GHz band in the USA and
5.9GHz band in Japan as well as in Europe.
Figure 2 DSRC-WAVE architecture
FCC allocated the band for DSRC for interoperability
point of view in 2004. This communication has applications
in both the safety and non-safety domain (Ezell, 2010).
Severe contention occurs, when multiple vehicles try to
rebroadcast the same message in the same zone of interest
(De Souza et al., 2017a; Ezell, 2010). This contention in the
channel leads to packet collision in the MAC layer. This is
known as the broadcast storm problem of the IEEE802.11p
protocol.
2.2.2.6 Probe devices
Taxies and government vehicles come under the probe
vehicles categories, these vehicles are equipped with
various wireless technology and DSRC standards.
Collectively, there are four ways by which one can collect
the raw information:
1 triangular method
2 vehicle re-identification method
3 GPS method
4 smartphone-based rich monitoring method.
These vehicles are responsible for monitoring the speed of
vehicles in any area. Vehicles send this information to a
central entity called the TMC, where all the data gets
integrated, and a picture of the traffic flow gets created of
the city. This can be used to find congested spots. Now,
mobile vehicles equipped with GPS are doing this job. Wide
coverage, cost efficiency, and easy maintenance are the
advantages of probe devices over other technologies. In
Beijing, there are more than 10,000 commercial vehicles
and taxies have inbuilt GPS chips that are responsible for
the transmission of the speed of vehicles to the satellites.
Further, satellites send information to the central server.
2.2.3 Commuter-based data collection
The third data collection technique is commuter-based data
collection. The two-wheeler commuter uses smartphone
applications and social media to provide updated traffic
information voluntarily. Currently, the Waze cell phone
application operated by Google makes use of the traveller’s
location to predict the change in traffic flow, and the
location of any unusual traffic incident. This data comes
under the category of semi-structured data. This category is
not that much reliable as it is not possible to know the exact
location of the incident because everybody doesn’t activate
their geographical functionality.
2.2.3.1 Twitter
Twitter is a social media platform having text and image
data used to detect traffic congestion in real-time scenario.
This data plays an important role in real-time applications
such as real-time alerts, incident detection. Many
researchers have proposed the machine learning model that
uses Twitter data for decision-making. But lower location
precision is the major issue with social media data.
2.2.3.2 Waze
Waze is considered a subsidiary of Google that provides
access to satellite navigation software. It is compatible with
smart phones and computers having inbuilt GPS support. It
gives the turn-by-turn monitoring knowledge and user-
submitted times and path information, during the
downloading of location-based data over the mobile cellular
network. It is considered free to download and use GPS
navigation applications.
2.2.4 Geographical-area data collection
An approach that monitors the traffic flow with the help of a
network of multiple sensors is the fourth data collection
source for the ITS application. Technologies used for data
collection are majorly used are photogrammetry and video
recording with the help of unmanned aerial vehicles and
space-based radar. Information such as vehicle spacing,
density, and speed can be extracted from this data which is
useful in the ITS applications such as traffic monitoring and
incident management.

Introduction to intelligent transportation system 221
Table 2 Comparison of various ITS technologies
Data sources Technologies used Traffic parameters Data type Advantages Disadvantages
1 Larger coverage range
compared to loop detectors
1 Majorly affected by
weather conditions
CCTV cameras Volume, speed,
classification, headway,
presence
Structured
2 Performance is independent of
the traffic load
2 Extendable life cost
1 Independent of weather
conditions
1 Limited coverage
range
2 Can be damaged by
heavy vehicles
Infrastructure-
based data
Loop detector Volume, speed,
classification, headway,
presence
Structured
2 Availability of skilled
manpower because of its
generality 3 Extendable life cost
1 Larger coverage range
compared to both loop
detector and CCTV cameras
1 Extraction of data
can only be done by
sophisticated
algorithms
2 No specific hardware
equipment is required in
vehicles
3 No infrastructure is required
along the roadside
GPS + cellular
network (Floating
car data)
location, travelling time,
speed, acceleration/
deceleration, incident
detection
Structured
4 Independent of weather
conditions
2 Less location
precision through
GPS
1 Larger coverage range
compared to both loop
detector and CCTV cameras
1 Extraction of data
can only be done by
a sophisticated
algorithm
2 No specific hardware
equipment is required in
vehicles
Vehicle-based
data
Connected/
cooperative
vehicles
Location, travelling time,
speed, acceleration/
deceleration, incident
detection
Structured
3 Independent of weather
conditions
2 DSRC and other
communication
devices are
mandatory required
1 Dependent on
weather, vegetation,
and shadows
Geographical
area-based data
Photogrammetry Flow monitoring,
transportation planning
and design, and incident
management
Structured Majorly collects the data from the
locations that are not easily
accessible from the ground
2 Accuracy is camera
quality and flying
height dependent
1 Location precision
is low
Commuter’s-
based data
Twitter, Waze
application
Real-time alerts, incident
detection
Semi-
structured
Cover large area due to presence
of commuters
2 Semi-structured
data
2.2.4.1 Photogrammeter
Photogrammetry is the technique used to collect information
regarding the vehicles from the images taken from various
locations and angles. This technique plays an important role
in the generation of the 3D scenario of real-world and
geographical maps. The triangular method is the basic
principle used by photogrammetry. Aerial photogrammetry
is the basic approach used to map an area using an
unmanned aerial vehicle. Terrestrial photogrammetry can be
done by placing the camera’s axis parallel to the earth.
Theodolites are the instruments used for this purpose. Space
photogrammetry can be done by placing the camera on an
artificial satellite.
Table 2 shows the comparison of various data collection
and dissemination technologies used in various ITS
application based on applications, data type, and their
advantages and disadvantages.
3 The architecture of TMS, ITS, and VANETs
This section gives a detailed introduction to the architecture
and data flow of the TMS, ITS, and VANETs.
3.1 Architecture of TMS
The architecture of the TMS is shown in Figure. 3. The
main objective of TMS is to regulate the traffic in
cities/urban areas/highways and to help commuters to reach
there quickly and safely at their destination. The traffic
control centre (TCC), ITS, various sensors such as GPS and

222 S. Sharma and S.K. Awasthi
in-road ones, and CCTV cameras are the major elements of
TMS. The various types of cameras and sensors are
responsible for data collection of present state traffic
scenarios in a busy urban city. This collected data is
transferred to the ITS module that intimates the TCC.
Periodically after two seconds, the TCC system evaluates
whether it is required to adjust the activity of traffic or to go
with this condition as it is. ITS is the main module of TMS,
it is responsible for the management of traffic mobility,
driver’s assistance, and enhancement of transport-related
infrastructure (Mollah et al., 2021) The integration of
advanced information and communication technologies can
also be termed ITS. This system is designed for the sake of
embedded search and acceptable implementation of the
scenario that will look after the mobility of vehicles and
driver’s safety. Figure 4 shows the flow of information/data
from a transportation system to an ITS user. The data flow
in ITS is sub-divided into four stages:
1 data acquisition
2 data processing
3 data communication
4 data utilisations.
The data acquisition phase is responsible for data collection
by monitoring the real-time traffic flow with the help of
various sensors such as inductive loop detectors, video
inductive loop detectors, video image detectors, and CCTV.
Collected data from various sources at the data management
centre is then required to be processed. After that, it is
verified and consolidated into the desired format that is
needed by the operators. An automatic incident detector
(AID) is usually used for data processing, whereas GPS in
vehicles is also used for this purpose. Several
communication technologies either wired or wireless are
used by data management centres.
Figure 3 Architecture of traffic management system
Data
Collection
Data
Storing
Data
Transfer
Traffic Control
AHS
Traffic Control
Center
Traffic Management
Auto-fixation of
traffic violatio
n
Real-time traffic conditions
monitoring
Providing traffic
participants with
IMS
Traffic light
control
APTS ATIS ATMS CVC
Intelligent Transporta tion System
Smart Traffic
lights
Toll
booths
Data collection module
Data
GPS sensor In-road Sensors Car Park Sensor
CCTV Cameras
These technologies are used on the vehicle side for
electronic toll collection, parking management, in-vehicle
signing, and in route guidance. The Data distribution
module is responsible for the distribution of traffic and
weather-related data for the improvement of traffic
efficiency, safety, and to make eco-friendly traffic flow.
Figure 4 ITS information chain (see online version for colours)
3.2 Architecture of ITS
Knowledge of the framework of ITS application is a
primary requirement in perceiving the various system
elements of ITS. ITS architecture provides a blueprint for
planning, defining, and implementing different ITS
applications. ITS architecture is also responsible for
defining the information flow in the system and standards
associated with it to provide specific ITS services. The USA
national ITS architecture is in very general form that
provides basic guidance to secure the interoperability of the
systems, elements, and services. Secured interoperability
through standardisation leads to the deployment of ITS
functionality even after the advancement in information and
telecommunication technology. the USA department of
transportation (USDOT) at first in the globe defines and
developed the national ITS architecture standards in 1993.
The development of an integrated architecture of ITS for the
city that makes use of national ITS architecture can support
shared data resources and national standards. Consequently,
this leads to a reduction in costs and labour for data
collection, processing, data dissemination, and effort
duplication while implementation of various ITS
applications. Countries like the USA, Japan, and Europe
follow the national ITS architecture to ensure the
interoperability and compatibility of various ITS
components in different ITS applications. As per the
European telecommunication standards institute (ETSI)
architecture of ITS comprises three layers namely:
1 facilities layer,
2 networking and transportation layer
3 access layers (Ali et al., 2018).
Analogous to the OSI reference model, the facilities layer’s
functionality is equivalent to the functionality of the
application, presentation, and session layer. The networking
and transportation layer is similar to the network and
transport layer of the OSI model. Similarly, the access layer
acts equivalent to the data link and physical layer. The
access layer is responsible for the transmission and
reception of messages. This layer suffers from various
attacks such as, Node capture attacks (Zhao et al., 2016),
malicious code injection attacks (Yanget al., 2015), False

Introduction to intelligent transportation system 223
data injection attacks (Yang et al., 2015), replay attacks, and
interference and eavesdropping (Yang et al., 2015). The
network and transport layer are responsible for data
transmission between RSUs, and central servers. Security of
ITS critically suffers due to this layer as it suffers from
denial of service (DoS) attacks, spoofing attacks, sinkhole
attacks, wormhole attacks, man in middle attack (MIMA),
and Sybil attack (Ali et al., 2018). The facilities layer is
responsible to provide the services based on user-request.
Phishing attacks, Malicious viruses, Malicious scripts are
the issues that affect the security in ITS (Ali et al., 2018).
The major difference between the USA and European ITS is
that US-ITS architecture is designed for surface
transportation that includes submarine, railway, and road
transportation (USDOT, 2019) whereas European ITS was
designed for road transportation (Mandžuka et al., 2013)
Figure 5 ITS architecture (see online version for colours)
Broadly the National ITS architecture employs three layers,
namely:
1 procedural
2 conveying
3 communication layers.
The procedural layer is responsible to define the policies,
financial incentives, and processes to give the organisational
support and to reach appropriate decisions. The conveying
layer is the key component of ITS architecture having the
responsibility of various transportation services such as
haulage signal preference and transport safety monitoring.
This layer is composed of subsystems, functions, interfaces,
and data definitions for every transportation service. The
communication layer in national ITS architecture is
responsible for defining the various communication
technologies and services to support the ITS applications.
Figure 5 commutatively shows the architecture of ITS.
User services and requirements of user services,
analytical architecture, physical architecture, service
conglomeration, security, and standards are the basic
components of national architecture. All these components
are explained below in this section.
3.2.1 User services and requirement of user services
In the ITS architecture, user services are the basic building
block that defines the working of the system. User services
are defined from the users’ or stakeholders’ point of view.
At the initial stage, these services were described by
USDOT by considering the input of various stakeholder
communities. User services provide high-end transportation
services that resolve the identified transportation issues.
Initially, there were 29 user services defined but at present,
there are 33 user services and are grouped under eight broad
areas namely:
1 travel and flow management
2 passenger transportation management
3 electronic toll payment
4 commercial vehicle operation
5 emergency incident management
6 safety management of the vehicle
7 information monitoring and management
8 construction and maintenance service.
It is mandatory to define the group of functions to attain
these user services. Consider the case of defining the
roadway speed that relies on traffic scenarios. The roadway
speed can be defined by gathering traffic-related data by
monitoring the traffic flow. A group of functional
statements use to define these various functions, is named
user service requirement. In case an agency wants to
perform any function that is not pre-existent then there is a
requirement of defining a user service requirement. The user
service requirements are very much mandatory for the
functional processes’ development and data flow of various
ITS services.
3.2.2 Analytical architecture
The analytical architecture is traced by a group of actions,
processes, data, and data flow as an acknowledgment of the
various user service requirements in the ITS architecture.
The major goal of the analytical architecture of ITS is to
explain functional processes and information flow of the
ITS, and also to generate the functional requirements for the
latest ITS services. The analytical architecture is technology
and implementation independent. It has nothing to do with
the element that is performing the function, location i.e.,
where the function is being performed, and the process that
is being used to perform the function.
3.2.3 Physical architecture
By considering analytical architecture, the development of
physical architecture is being done and is constituted of
various physical elements and the architectural flow. The
physical architecture act as a blueprint to the transportation

224 S. Sharma and S.K. Awasthi
system that assigns the responsibilities to the various
subsystems and the terminators in ITS architecture. The
subsystems are considered as the physical entities of the ITS
architecture and are further divided into four groups,
namely:
1 centre, responsible to provide particular services for the
transportation network including administrative,
management, and support services
2 roadway subsystems, placed along with the road
network responsible for monitoring, information
provision, and control services
3 cooperative vehicles, provides the commuters
information and safety services
4 travellers that use mobile gadgets to access the ITS
services during their trip.
The information flow from the analytical architecture goes
from one subsystem to another subsystem. Information
flows are arranged together into the architecture flow as
shown in Figure. 5 to show the physical architecture of ITS.
In Figure 6 green boxes and gray boxes represents the
terminators and functions respectively. Geographical map
update providers, basic moving vehicles (probe vehicles),
and location data sources are considered as a terminator in
physical architecture. The subsystems and terminators
require the interface and data communication and it is
defined by the flow of architecture and their communication
standards.
Figure 6 Physical architecture of ITS (see online version
for colours)
gy
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Providese‐
paymentservices
Provide
commuter
servicesEmergency
service
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Emergency
communication
system
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Vehicles
Transit
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Archiveddata
consumersystem
ArchivedData
management
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managementStoragefacility
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Vehiclemaintenance
monitoringmanagement
andcontrol
3.2.4 Service Conglomerations
Service conglomerations provide a service-based structure
of ITS architecture. These conglomerations are designed to
address the real scenario transportation issues. Service
packages inside the physical architecture address services.
For clarification in the case of moving vehicle monitoring
service, the moving vehicle monitoring service
conglomeration is responsible for monitoring. Service
package as a group of integral subsystems, component
packages, terminators, and architecture flow provide the
specific services. The service package for vehicle
monitoring service constitutes of four different subsystems,
namely:
1 the data service centre
2 traffic management
3 vehicle monitoring
4 transit vehicles.
Service conglomeration also employs three terminators,
namely:
1 basic moving vehicle
2 geographical map update providers
3 location data sources.
The traffic management subsystem is responsible for three
functions namely:
1 processing the data of vehicle geographical position
2 updating the transit route
3 providing real-time data to other subsystems.
3.2.5 Standards
Standards are mandatory to interconnect the independently
operated elements to provide an interoperable ITS.
Standards are responsible to check the interoperability at
local, regional, and national levels, without getting affected
by advancements in technology. Analytical and physical
architecture act as the foundation in the development of
standards. The defined architectural flows and the data flow
from the physical architecture and the analytical architecture
respectively, and the process of information exchange at
interfaces is required to be standardised. Several
organisations namely: the American association of state
highway and transportation officials (AASHTO), the USA
public transportation association (APTA), the USA society
of testing and materials (ASTM), the national electrical
manufacturers association (NEMA), and the society of
automotive engineers (SAE) are involved in ITS
standardisation.
3.2.6 Security
Security is considered as the prevention of available
transportation infrastructure and data. At present to gather
the data and broadcast purpose, the transportation system is
prominently information technology-dependent for the
advancement of mobility and safety of the entire ITS. In the
ITS architecture, the security component is divided into two
parts:
1 Safe ITS
2 ITS safety domains.
Safe ITS is the key behind the ITS security system.
Information certainty, ITS personnel security, operation
safety, and security management are the four different
components of safe ITS. Whereas disaster management,
freight, and commercial vehicle safety, wide-area alert, fleet
management, transit security, and transportation and

Introduction to intelligent transportation system 225
infrastructure safety are the various ITS safety domains.
Consider a case of the surveillance system for better
understanding which consists of a control unit and CCTV
camera. The control unit is responsible to control the
functionality of cameras without disclosing the image to any
unauthorised person from the perspective of safe ITS.
Whereas in the case of the safe ITS domain we consider the
transit surveillance system which is responsible to provide
the deterrent output tool to enhance the transportation
system security, also known as Transit security.
3.2.7 VANETs
VANETs is the most promising technology used by ITS.
For the management of high-speed vehicles, that use small
communication time among themselves for data transfer,
VANETs create an ad-hoc network for short-range
communication (De Souza et al., 2017a). VANET addresses
the issue of permitting interoperable networked wireless
communications among vehicles, infrastructure, and
personal communication gadgets.
Figure 7 Vehicular ad-hoc network (see online version
for colours)
VANETS uses vehicle-to-vehicle (V2V) communication
in which vehicles can directly send and receive data to and
from the neighbouring vehicles respectively, without
any infrastructure support. To establish the
communication between vehicle and roadside infrastructure,
vehicle-to-infrastructure (V2I) communication is used.
Another type of communication is the vehicle to everything
(V2X) communication. This type is used to extend the
coverage area of roadside infrastructure, by multi-hop data
for- warding to the vehicles that are not in the range of that
RSU. Figure 7 shows how VANET establishes the link for
communication between vehicles, infrastructure,
pedestrians, and sensors. VANET uses DSRC that operates
at 75MHz from 5.850 to 5.925 GHz having 7 licensed
channels (1 control channel +6 service channels).
DSRC uses the IEEE802.11p protocol for establishing
communication among vehicles. Nowadays this protocol is
named wireless access for the vehicular environment
(WAVE) used for safety and non-safety applications.
Visible light communication (VLC) is the latest technology
used by VANETs.
4 Classification of ITS based on physical
architecture
The physical architecture model is the integration of the
system elements and physical interfaces. It is required to
satisfy the analytical architecture components and
requirements. Various functions from the analytical
architecture that addresses the same issue are combined into
subsystems. A physical entity is developed to provide the
functions with the help of subsystems. Figure 6 represents
the physical architecture having communication between
various physical entities.
The physical architecture of ITS can be divided into
three groups by considering the communication between the
physical entities for traffic management in terms of safety
and non-safety applications, namely:
• centralised
• distributed
• hybrid
This section gives information about these three models.
4.1 Centralised ITS
A centralised transportation system uses vehicle-to-
infrastructure (V2I) communication for the management of
safety and non-safety applications. In these models, the
vehicle sends information like speed, current location, and
destination to the central server via nearby RSUs for the
alternate route computation. Figure 8 shows the centralised
mechanism for traffic flow control. The central entity
computes the shortest available routes for vehicles to avoid
congestion (Rakha and Kamalanathsharma, 2011).
Figure 8 Centralised ITS (see online version for colours)
Many researchers have proposed ITS models based on a
centralised architecture. Out of them, a few are explained
here in this section.
4.1.1 Re-RouTE
Guidoni et al. (2020) proposed Re-RouTE, which is a novel
service for traffic management for the reduction in
congestion in dense urban areas. In Re-RouTE, the central
server uses density for the congestion computation. Re-
RouTE deploys the basic conceptualisation of the flow
density model of the Traffic Engineering theory. For

226 S. Sharma and S.K. Awasthi
classification of traffic congestion, this is divided into four
modules:
4.1.1.1 Location information
This is responsible for the collection of vehicle information
in terms of their speed and location
4.1.1.2 Network representation module
It creates a weighted graph by considering the information
received from vehicles and the city map network
4.1.1.3 Network classification
It informs whether a route is congested or not
4.1.1.4 Route suggestion phase
It computes the alternate path for the vehicles are stuck into
the jam or are about to stick. Even in the scenario where
commuters do not accept route suggestions, Re-RouTE can
reduce traffic jams. It uses reactive routing, which leads to a
high latency rate in route computation.
4.1.2 Eco-driving:
The ITS model named Eco Driving presented by Rakha and
Kamalanathsharma (2011) uses inbuilt sensors in traffic
lights as smart infrastructure. This model helps the
commuter to avoid the waiting time at the intersection
(because of traffic light signal) thus helping them to save
fuel consumption and decreasing CO2 emission
significantly. To achieve this goal, vehicles adjust their
speed and headway. The eco driving model sends the signal
phasing and timing (SPaT) information to the vehicles that
are approaching the traffic light. SPaT information includes:
1 the state at which the traffic light is currently at i.e.,
red/ yellow/ green
2 the string of the vehicles at the intersection to pass
2 the time left for the next phase of the traffic light to
occur.
After receiving the SPaT signal, with the help of the
VT-micro model, the vehicles compute the optimum speed
to save fuel consumption.
4.1.3 Decision-tree-based green driving suggestion
system (DTGDSS):
DTGDSS is a model suggested by Lee et al. (2012), based
on V2I communication. This model helps to make a
significant reduction in CO2 emission, by providing the
speed limit and best optimum path to commuters with the
help of a decision tree. This model has inbuilt RSU in all the
traffic lights. Vehicles send their speed, location, and
direction to these RSUs. Upon receiving these pieces of
information from vehicles, RSUs compute the delay queue
length at the intersection in all directions. Every traffic light
broadcasts its current light phase, length of the waiting
queue, and the time left in the occurrence of the next light
phase. Vehicles calculate their speed with the help of a
decision tree and opt for the optimum speed to avoid the
waiting duration at the traffic signal. In this model, the
decision tree constitutes a six-layered structure for the
determination of recommendation:
1 the speed for a car
2 speed of a car in front of it
3 speed maintenance
4 braking
5 gas pedal
6 headway.
4.1.4 Re-routing strategies:
Pan et al. (2012) proposed three re-routing strategies for the
reduction of traffic congestion. In these strategies, vehicles
send real-time data in terms of position and speed to the
central entity. Authors proposed re-routing techniques,
named as:
1 dynamic shortest path (DSP)
2 random k-shortest paths (RkSP),
3 entropy balanced k-shortest path (EBkSP).
In DSP, the shortest path to the destination among all is
provided to the vehicle. In RkSP, the server computes the k-
paths to the destination for a vehicle and assigns a random
route, but the route is not optimal. In the case of EBkSP, the
server computes k-paths based on an entropy-based
algorithm to provide the least popular path to avoid further
congestion. EBkSP performs best among the three models.
Computation cost is high at the server end. The models
work in four phases and that are collection and
representation of data, detection of congestion, vehicle
selection for re-routing, and alternative route finding.
4.1.5 Traffic detection and control
Celso et al. (2015) presented a model for congestion
detection and traffic control. All the vehicles in this scenario
are connected to RSUs. Distribution of RSUs, collection of
data, and detection-and-control of congestion are the
working phases of this model. In the distribution of RSUs
phase, the task is to find the location for the RSUs
installation, so that they can cover the maximum
geographical range. The data collection phase makes RSUs
collect data from the vehicles that are in the range of that
particular RSU. The vehicles send their information in terms
of ID, average speed, direction, and time taken by vehicle to
move from any road towards the destination in past to the
nearby RSU. Vehicles interact with RSUs through single
hop by using LTE or 3G as a communication mode. At the
congestion detection-and-control phase, each RSU makes a

Introduction to intelligent transportation system 227
directed graph that has the intersections as vertices and
connected roads as edges and then calculates the weight of
each road. RSU then computes the k- shortest path towards
the destination. Further, RSUs broadcast the route based on
Boltzmann probability distribution to the vehicles, and the
rest of the leftover routes are added for the computation of
the new route.
4.1.6 A solution using co-operative rerouting to
prevent congestion (SCORPION)
For traffic congestion detection and reroute planning for the
congestion control, De Souza et al. (2015a) proposed a
centralised approach named SCORPION. This approach
considers the roads as directed graph G = (V, E), where V
defines the set of intersections, and E represents the set of
connected roads. In this model, the vehicles send their
information such as their speed, location, direction, ID to
the central server through single hop using long-range
communication (4G and LTE).
Figure 9 Distributed ITS (see online version for colours)
The working is divided into two phases, in the first phase
congestion has been detected and weights are assigned to
the roads for the sake of congestion classification and in the
second phase, alternate routes are being assigned to the
vehicles. This model uses a KNN classifier for the
identification of congestion on any particular road. The
average speed of the road is considered as the input to the
classifier and the level of congestion is considered as the
output. It uses the cooperative algorithm for re-routing of
the vehicles. This is the greedy algorithm and assigns the
lowest weighted road to the vehicle and updates the system
periodically. CHIMERA is another TMS model proposed
by De Souza et al. (2016) which is similar to SCORPION
except it uses a probabilistic k-shortest path algorithm for
the computation of alternative routes.
4.1.7 Ad hoc on-demand distance vector (AODV)
Barba et al. (2012) suggested an ITS model in which the
sensors in traffic lights collect real-time data from the
vehicles that are in the coverage range. Traffic lights
evaluate the collected data in terms of density, weather
conditions along road accidents occurred. Further, traffic
lights broadcast this information to all the vehicles that are
available in their coverage areas. Real data sent by vehicles
to traffic lights is in terms of the current position, speed,
number of nearby vehicles. These messages are being sent
every two seconds by a data dissemination protocol named
AODV. After receiving data, the traffic lights update their
statics with the help of an exponentially weighted moving
average (EWMA). This model classifies the congestion in
three levels i.e., free, semi-congested, and very congested.
4.1.8 Adaptive next road re-routing (a-NRR)
Wang et al. (2016) proposed a system named a-NRR for the
avoidance of congestion in urban cities. This model saves
cost as it obtains the global condition of traffic with the help
of information available at RSU installed at intersections.
The a-NRR protocol gives the next best path towards the
destination rather than a complete destination path. Each
RSU sends the information to the traffic operation centre,
and then this sends the information to the RSU where the
congestion has occurred. RSU broadcast this information to
the vehicles. Then each vehicle verifies whether it is going
through that area or is avoidable.
Table 3 shows the comparison of different centralised
ITS models based on congestion detection and re-routing.
Many researchers have proposed ITS models based on a
distributed architecture. Out of them, a few are explained
here in this section.
4.2.1 A self-organising traffic information system
(SOTIS)
Wischoff et al. (2003) presented a TMS named SOTIS,
which is one of the first TMS that use V2V communication.
Each vehicle has the surrounding information in terms of
traffic flow and location. All information is being shared
with the other vehicles within the same area. This TMS uses
a store-carry-forward technique for sharing road
information with vehicles that are in other areas. This
system is not that much efficient because of message
overhead.

228 S. Sharma and S.K. Awasthi
Table 3 Comparison of centralised ITS models
Model Congestion detection strategy Re-routing algorithm Remarks
Re-route Guidoni et al. (2020) Vehicle’s flow density Shortest path Unable to do proactive rerouting
Eco-driving Rakha and
Kamalanathsharma (2011)
SPaT information of the
vehicle
Dijkstra Algorithm Multiple overlapping of routes
DTGDSS Lee et al. (2012) Delay queue length Decision tree-based Compromise with scalability
DSP Lee et al. (2012) —– Route with less travel time Causes congestion in future
RKSP Pan et al. (2012) - The random path out of k paths May not provide the optimal
path
EBKSP Pan et al. (2012) - The entropy-Based least popular
path
May cause congestion as it
assigns the least used path
SCORPION De Souza et al.
(2015a)
KNN classifier Greedy algorithm May not provide the optimal
path
CHIMERA De Souza et al.
(2016)
KNN classifier Probabilistic k- shortest path
algorithm
May cause congestion as it
assigns the least used path
a-RR Wang et al. (2016) Speed of vehicles Next road rerouting algorithm Compromise with scalability
4.2.2 Cooperative traffic congestion detection
Bauza and Gozalvez (2012) proposed cooperative traffic
congestion detection (CoTEC), which is a novel cooperative
vehicular system. This approach is mainly used to identify
the congestion on a road. CoTEC uses fuzzy logic for this
purpose. For the generation and broadcasting of local
information of the vehicles, CoTEC uses beacon messages
or cooperative awareness messages (CAM). It generates a
message periodically every 2 seconds. This approach also
uses fuzzy logic to identify the congestion level for each
vehicle locally. This fuzzy logic-based system comes into
appearance by using the level of service (LOS) that is
mentioned in the highway capacity manual (HMC). The
LOS is defined as the parameter which is used to evaluate
the performance of the transportation system from the
traveller’s point of view. Whenever the vehicles come
across the congestion, each vehicle broadcasts the
information regarding that congestion according to their
judgment. With all these collective judgments, vehicles
identify and then characterise the whole traffic congestion.
4.2.3 Cooperative vehicular traffic congestion
identification and minimisation (CARTIM)
Araujo et al. (2014) proposed CARTIM, which is a
technique to first identify the traffic congestion and then
minimise that congestion. Its main purpose is to reduce in
traveling time of vehicles while facing road congestion. In
this approach, vehicles periodically send speed and density
as the beacon messages. With the help of these data, after
applying the fuzzy logic, the system will suggest changing
the route for avoiding the congestion on road. As the
overhead is more in the case of CAM signals as compared
to beacon signals, this approach makes sure the limited use
of the CAM signal for broadcasting purposes. This approach
also provides a reduction in CO2 emission and makes the
ride eco-friendly.
4.2.4 Data dissemination in highway environments
with different traffic conditions (DRIFT)
Villas et al. (2014) presented a V2V ITS that minimises the
congestion due to accidents at the highway. This approach
takes care of the broadcast storm issue present in VANET
during data dissemination. This TMS employs the DRIFT
protocol for broadcasting the warning regarding the
occurred accident on the current road. This protocol enables
the TMS to cover the maximum range with low overhead,
high efficiency, and a minimum possible delay. As soon as
the vehicle receives the warning/alert message, they change
their route to avoid the congested route. With this approach,
one will be able to reduce the travel time, fuel consumption,
and also emission of CO2.
4.2.5 A data dissemination (ADD) protocol
Souza et al. (2014) proposed a TMS for the control of road
congestion occurrence in dynamic highways because of
road accidents. There is an undesired effect called
synchronisation in IEEE802.11p, due to this effect many
vehicles start broadcasting the message at the same time.
This effect is very severe in a highly dynamic environment.
This ITS approach uses channel switching for this purpose
i.e., in 50 ms there is a switch from CCH to SCH, for data
transmission it requires SCH to be in an active state. This
technique also provides the accident alert message as a
broadcast message to every vehicle by using ADD protocol.
This protocol is based on the delay concept to minimise the
effect of broadcast storming as well as the synchronisation
effect that was occurred due to IEEE802.11p in a dynamic
highway scenario. It uses the preference zone concept to get
rid of the broadcast storming problem. To address the
synchronisation issue, it uses the desynchronisation method
by knowing the concept of channel switching. After
receiving the alert message, the commuter will decide that
the route they are following is going to that accident area. If
yes, then they will change their route to save travel time and
also fuel consumption.

Introduction to intelligent transportation system 229
4.2.6 Urban congestion detection system
(UCONDES)
Meneguette et al. (2015) suggested the UCONDES model
for the detection and reduction of road congestion in urban
areas. ANN technique is used by the authors for the
detection and classification of different types of congestion
levels in urban roads or arterial networks. This system takes
the average speed and density of the road on which one is
driving as an input. This input is provided by the vehicles
through the beacon signals. With the help of ANN,
UNCONDES provides the level of road congestion as an
output. In this approach, the authors describe the congestion
level in three parts:
1 less than 0.3 (free flow)
2 between 0.3 to 0.7 (moderate flow)
3 greater than 0.7 (congested road).
Once the prediction is done about the congestion level of
the current road, then this information is broadcasted to the
vehicles using beacon signals. Once the vehicle gets to
know about the road condition then the commuter will
decide to continue with the present road or to change the
route. This technique does not use any broadcast
suppression method to limit the network overload which
leads to an increase in overhead, which will result in
degradation of network efficiency. Also, this method is not
able to provide the alternate route’s road conditions and will
provide the congestion level of that road on which one is
going.
4.2.7 FASTER
De Souza and Villas (2016) come up with the fully
distributed TMS, known as FASTER for the betterment of
efficiency, reduction in overhead, and network overload.
This approach divides the entire network into small districts.
FASTER uses a k-mean clustering algorithm for this
purpose. In this approach, the vehicles share the local
information with the neighbour vehicles within the district
using beacon messages. These messages consist of the
average speed and location of that vehicle. After that, each
vehicle develops a knowledge of the entire traffic within the
district by using those received beacon messages from
various neighbours. The district knowledge consists of all
roads and their average speeds within the district. Building
the overall knowledge of traffic conditions requires inter-
district communication. This technique takes care of the
selection of transmitter, which means which vehicle will
broadcast the district knowledge to other vehicles in the
network. In this scenario, to overcome the issue of the
broadcast storm, the vehicle that is near the centroid will
broadcast the district knowledge.
For packet collision minimisation, FASTER uses de
synchronisation mechanism. When vehicles receive all the
district knowledge, they will make a graph G = (V, E),
where V is vertices or the set of intersections where two or
more roads join and E is edges or set of roads in any given
network. Each edge comprises its specific weight depending
upon the average speed of that road. With the help of this
information, the commuter will be able to identify the
congestion on a current road. This technique provides the
information of the whole network which helps the
commuter to take other alternative paths for the avoidance
of traffic jams. Every vehicle goes for the probabilistic k-
shortest path algorithm, for avoiding the congestion on the
alternative opted routes.
4.2.8 A context-awareness protocol (CARRO) for
data dissemination
Akabane et al. (2020) suggested a new protocol for data
dissemination named CARRO. In this protocol, the
broadcasting node is the vehicle that lies in the dense traffic
area in an urban scenario. This approach is based on the
store and carries concept, i.e., if there is some other vehicle
that provides better traffic knowledge and covers more area
then that will become the next relay node or broadcasting
node. But it will increase the overhead as each vehicle
follows one-hop communication with the neighbors and
delay is another constraint in this case.
4.2.9 Preventing traffic congestion through a
fully- distributed rerouting algorithm
(PANDORA)
De Souza et al. (2016) proposed PANDORA, which is a
fully distributed ITS model. It provides better and precise
knowledge about the traffic in certain areas by considering
the point of network overloading. This approach uses the
floating zone concept to provide the entire traffic
knowledge to the vehicles present in that particular area.
This model gives better results in terms of efficiency,
coverage, overhead as compared to other ITS models.
4.2.10 Complex networks for data dissemination
protocol among vehicles (CORONOS)
VANET uses short-range communication and a highly
mobile network. That consequently makes it difficult to
spread the real-time traffic information regarding the traffic
flow among the vehicles in the network. Costa et al. (2018)
proposed a solution for this problem named CORONOS.
This approach helps the TMS in data dissemination with
low overhead and maximum coverage. Here vehicles create
the local knowledge by considering the one-hop and two-
hop communications for up to 2m of diameter. Degree
centrality and the betweenness centrality are the two factors
on which the CORONOS technique chooses the relay node
for data dissemination. Degree centrality is defined as how
many neighbours are connected to that node. Whereas the
betweenness centrality talks about the minimum number of
paths going through that node. According to simulation
results by applying CORONOS as a data dissemination
technique, ITS can improve the efficiency of the system by
95%, waiting time or congestion time during traffic can be

230 S. Sharma and S.K. Awasthi
reduced up to 48.95%, and trip time can be reduced by
18.11%.
Similar to CORONOS, data dissemination protocol
based on centrality (DDBC) is another data dissemination
protocol proposed by Costa et al. (2017) in which every
vehicle has the local knowledge about one hop and two-hop
neighbours. Further, the relay node broadcasts the traffic
information in two parts. In the first part, it covers the
vehicles that are between 0° to 180°, and in the other part, it
covers the vehicles in the 181° to 360° range. This approach
is good in terms of overhead and travel time but not that
good in terms of coverage.
4.2.11 Packet payload combining relaying (PCRL)
Reliability is one of the most severe issues in the case of
V2V based ITS when it comes to safety. By adding the
relay stations at the intersection of roads, it ameliorates the
reliability of a network. But there is a problem of packet
congestion in this concept. Consequently, this issue leads to
deterioration in the data transmission rate at the relay station
and degrades the relaying task. To overcome this issue,
Trien et al. (2018a) presented a scheme known as PCRL.
This technique compresses the overheads available in the
broadcast messages. It uses adaptive modulation with the
coding scheme at the relay station. To overcome the hidden
terminal problem, the authors integrate the PCRL with the
time-division grouping method.
4.2.12 Sector zed assisted V2V
communication/payload combining
forwarding (SRV2VC/PCF)
In V2V communication the basic need is a safe and reliable
journey. The relays are added at the intersection to improve
the payload, but the performance of the relay station is
limited due to hidden terminals. To overcome this hidden
terminal problem, a sectorised antenna is employed but the
packet drop at RS is the major problem in this case. To
overcome this problem, Trien et al. (2018b) proposed a
payload combining forwarding (PCF) with sectorised
assisted V2V communication (SRV2VC/PCF) scheme. This
scheme can increase the reception rate at the relay station
and also gives great results from the reliability point of
view. In this approach, all the improvements are based on
the directivity of a sectorised antenna used. V2V
communication provides the best results when it comes to
congestion detection and avoidance by using DSRC.
Limited coverage range is the issue with this approach.
Multi-access interference and low transmission rate are the
other parameters that make this approach less efficient.
4.2.13 I am not interested in it (IAN3IP)
De Souza et al. (2019) proposed a fully distributed ITS
model named IAN3I. In this approach, all the information
any vehicle has sent to the vehicle that is interested in it.
This will result in lower overhead and higher network
efficiency. According to simulation results, IAN3I reduces
the number of transmissions up to 95% whereas it reduces
the collision in packets up to 98% and latency up to 55%.
This approach has an anchor zone (AZ) where there are
floating nodes that provide information regarding the
critical area efficiently.
4.2.14 Better safe than sorry (BSTS)
Vehicular mobility is one of the most important areas in
designing any TMS. From this point of view, De Souza
et al. (2019) proposed a re-routing algorithm named BSTS.
This algorithm is based on Pareto-optimality. It distributes
the traffic on different routes for the avoidance of
congestion. In this system, each vehicle has its OBU and it
is used to communicate with the infrastructure like RSU,
base stations, edge servers. A remote cloud is responsible
for the generation of all the traffic awareness-related
information globally. The police department and vehicle
nearby become the service provider for providing complete
knowledge regarding safety in particular areas. The
complete area is divided into small blocks and to reduce the
number of transmissions the vehicle near the centroid
transmits the whole information to all the vehicles present in
that particular block. By getting the knowledge of all the
roads in the network in terms of congestion, one can go for
the road with the least congestion. Information’s regarding
safety in terms of robbery, theft is being provided by
heterogeneous means i.e., police department, and social
media. Further BSTS system processes this information and
locates that area on the map. With the help of this
information, the commuter will be able to choose the
alternative route towards the destination.
4.2.15 Random jamming method:
Lyamin et al. (2019) suggested a method based on data
mining for the detection of jamming, DoS attack in case of
IEEE802.11p in V2V communication. This method
provides clear knowledge of traffic jams with the help of
periodic CAM signals that are exchanged during V2V
communication and are not received by the vehicles. This
detection is based on the IEEE802.11p protocol and also on
past occurred events in the V2V control channel. This
model provides the best results even by assuming that there
are some random jitters in CAMs transmission. In this work,
they evaluate the two jamming scenarios by using this
technique and the scenarios are random jamming model and
on-off model. Advanced cooperative adaptive cruise control
(ACACC):
When one talks about improvements in traffic flow on
the road where V2V communication links are being used, in
that case, cooperative adaptive cruise control (CACC) plays
a very important role and improves the performance of the
system. Whereas the performance decreases when the
vehicle ahead has information to send but the link gets
disconnected among two. To overcome this issue, Navas
and Milanes (2019) proposed a technique named ACACC.
This method gives great results in information exchange
(link establishment) and reduces the inter-vehicular gap by

Introduction to intelligent transportation system 231
maintaining the stability of strings of the vehicles ahead.
This controlling technique provided an integrated behaviour
among two CACC systems by employing different time
gaps based on the string location of a vehicle with an
available V2V link. Youla-Kucera parametrisation is used
to establish a stable link among two CACCs. Simulation
results show that ACACC provides better results as
compared to existing ACC/CACC algorithms.
4.2.16 Traffic detection technique
Wang et al. (2019) presented a distributed traffic detection
technique. Firstly, they construct a fuzzy logic-based
controller which takes the speed of vehicle and density as
input and gives the output in terms of the level of
congestion. After obtaining this local information, regional
congestion is detected based on a hypothesis test. Results
show that this method provides better accuracy as compared
to the geomagnetic coil approach as well as the CoTEC
approach and also this approach reduces the overhead. Once
the knowledge is made about congestion then it sends this
information to the desired vehicles to reduce the congestion.
The information is provided by a multi-hop mechanism with
the help of IoVs technology.
4.2.17 Intersection-based distributed routing (IDR)
scheme
Sun et al. (2020) focused on the design of rerouting
algorithms for an ITS using V2V communication in
developed area VANETs. This is the main research area as
the VANET performance is affected by the atrial areas,
urban and suburban areas. To overcome this issue, the
authors proposed the IDR scheme. This approach uses the
intersection vehicle fog (IVF) model to collect the
information of vehicles that are stopped at the signal. IVFs
are installed at the intersections so that they can
communicate among themselves with the help of fuzzy
logic. IDR uses ant colony optimisation for routing purposes
at an intersection using multi-hop communication. There is
high connectivity with the routing road, which will
consequently reduce the transmission delay and also
increase efficiency. Simulation results show that the
proposed mechanism is feasible and also achieves the
desired performance.
4.2.18 Anti-jamming technique
Feng and Haykin (2019) focused on anti-jamming inter-
vehicular communication in the network by considering the
power control along with the selection of channels. The
authors employed the cognitive dynamic system (CDS) for
the in-depth study of V2V communication. To address the
jamming problem, the authors used the cognitive risk
control (CRC) approach in an autonomous network. With
the help of reinforcement learning, power control is applied
to every perception-action cycle (PAC). By evaluating the
risk, a multi-armed bandit (MAB) is used to select the
channel whenever required. With this continuous
monitoring, vehicles will be able to avoid jamming and
hence improve the overall efficiency of the network.
4.2.19 On-demand
Gomides et al. (2020) proposed an ON-DEMAND traffic
management Model. It is further subdivided into three steps:
1 road traffic analysis
2 communication model
3 re-routing.
In the road traffic analysis phase, the function of traveling
distance and expected distance has been considered to find
the contention.
This approach uses both proactive and reactive
information dissemination. Adaptive knowledge sharing and
knowledge maintenance are the two phases of proactive
dissemination. Reactive dissemination works in information
request-response and alert mode. In the re-routing phase
vehicles find the best minimum cost path from the data
available
in the database. This approach provides a significant
decrease in travel time and an increase in average speed.
Network challenges include broadcast storm, knowledge
insufficiency, and overload control.
4.2.20 Distributed vehicle traffic management system
(dEASY)
Akabane et al. (2020) presented a three-layer architecture
model for traffic management known as dEASY. The three
layers include environment sensing and vehicle ranking,
knowledge generation and distribution, and knowledge
consumption. It uses the egocentric between ness matrix
(EBM) for the evaluation of vehicle rank that is based on
the Google PageRank algorithm. In the first phase, this
system will aggregate the speed and evaluate the weight of
every road according to HCM. In the second phase, it is
responsible for the computation of alternate routes using the
entropy-based algorithm. This algorithm uses the least
popular path to avoid further congestion.
The major issue with this model is the waiting time in
the computation of alternate routes while detecting the
congested spot.
Table 4 shows the comparison among various
distributed ITS models in terms of division of geographical
area, vehicles participation in data gathering, congestion
detection approach, and re-routing technology used.

232 S. Sharma and S.K. Awasthi
Table 4 Comparison of distributed ITS
Model
Area
sectorosation
Participant
vehicles
Congestion
detection
strategy
Dissemination
protocol
Relay-node
selection
Rerouting
strategy Remarks
COTEC Bauza
and Gozalvez
(2012)
No All vehicles
within the
coverage range
of 300 m
Fuzzy logic - Every vehicle
sends CAM
messages
- Reduces
communication
overhead
CARTIM Araujo
et al. (2014)
No All vehicles
within the
coverage range
of 300m
Fuzzy logic - Every vehicle
sends CTE
messages
- High message loss
due to lack of
acknowledgment
UCONDUS
Meneguette
et al. (2015)
No All vehicles
within the
coverage range
of 300 m
ANN -
Every vehicle
sends CAM
messages
Shortest path Increases network
overload and
decreases traffic
efficiency
FASTER
De Souza and
Villas (2016)
Yes All vehicles
within the
coverage range
of 300 m
Real-time
decision (LOS
of HCM)
Delay based Vehicle near
the centre of
the district
Probabilistic k-
shortest path
Overhead
decreased and
channel overload
increases
CARRO
Akabane et al.
(2020)
No 1-hop
Real-time
decision (LOS
of HCM)
Store and
carry
Vehicle
located in high
priority
geographical
area
- Overhead increases
and delay decreases
CORONOS
Costa et al.
(2018)
No 2-hop
Real-time
decision (LOS
of HCM)
Sender-based Between ness
centrality and
degree
centrality
- Overhead and
delay decreases
PANRODA De
Souza et al.
(2016)
YES All vehicles
within the
coverage range
of 300 m
Real-time
decision (LOS
of HCM)
Floating
content based
Vehicle in the
critical area
Probabilistic k-
shortest path
Increases traffic
efficiency, but
disseminate
Redunda messages
dEASY De
Souza et al.
(2016)
No All vehicles
within the
coverage range
of 300m
Real-time
decision (LOS
of HCM)
Delay based Egocentric
between ness
Entropy-based
shortest path
Waiting time in
congestion is the
issue
On-demand
Gomides et al.
(2020)
No 1-hop
Real-time
decision
Proactive
information
Vehicle at the
centre
Reactive
knowledge
discovery
Decreases average
travel time, but the
number of message
transmissions is
more
4.3 Hybrid ITS
To increase the coverage range and to provide an accurate
traffic view, hybrid ITS uses both V2V and V2I
communication as shown in Figure 10. This is best suited
for making the collaborative/proactive re-routing. In this
model, the central server acts as a coordinator that is
responsible for data collection and distributing the data to
other servers through the Internet. It helps the commuters to
decide on the next path that leads to their destination
without facing congestion DSRC protocol is used by the
VANETS for establishing the communication among
vehicles and also between infrastructure and vehicles, which
has low coverage area. To overcome this issue, Guchhait
and Debdatta (2018) proposed a combination of both
WiMAX and DSRC, which also resolve the issue of
multi-user detection (MUD).
Figure 10 Hybrid ITS (see online version for colours)
Many researchers have proposed ITS models based on
hybrid architecture. Out of them, a few are explained here in
this section.

Introduction to intelligent transportation system 233
4.3.1 Distributed vehicular traffic re-routing
(DIVERT) system
Pan et al. (2016) proposed a hybrid traffic management
model named DIVERT. This model has four features:
1 a scalable system architecture, for distributed re-routing
2 distributed re-routing algorithms, that make use of
VANETs for the computation of alternative paths for
the vehicles individually
3 privacy-aware, this will significantly reduce the
exposure of sensitive location
4 optimisations to reduce the VANET’s overhead.
This model balances user privacy with the re-routing
effectiveness. DIVERT increases user privacy by 92% on
average as compared to the centralised system. In terms of
the average travel time, DIVERT’s performance is slightly
less as compared to the centralised ITS systems. In addition,
DIVERT reduces the CPU as well as network load from the
server by 99.99% and 95%, respectively.
4.3.2 Faster and safer (FnS)
De Souza et al. (2018) presented FnS TMS. This system
provides complete knowledge regarding the congested area.
Furthermore, this approach talks about the safer area for
pedestrians and drivers without degrading the network
efficiency. It consists of a hybrid module that has a main
server and the OBU on every vehicle. Every vehicle
communicates either by V2V or by V2I mode and then the
information is provided to the main server. After that, the
server sends the congestion and road safety details to all the
nearby vehicles for re-routing to avoid congestion and make
the trip better from the safety point of view.
4.3.3 Hybrid model (WiMAX + DSRC)
Guchhait and Debdatta (2018) suggested a combination of
both WiMax and DSRC to maximise the efficiency,
capacity, and transmission rate with the help of MUD and
the routing technique. The authors proposed a scheduling
technique named as league championship priority
scheduling algorithm which is used to send messages based
on the priority. This algorithm depends upon the value of
bandwidth and coverage of the network so that multiple
access interference can be avoided. MUD is used to identify
active and inactive users based on threshold frequency and
bandwidth. The results of this model show the improvement
in data transmission rate and also MAI can be avoided
significantly.
4.3.4 Improvement of traffic condition through an
alerting and re-routing system (ICARUS)
De Souza et al. (2019) proposed a hybrid system named
ICARUS. This system receives information about the
hazards related to the traffic from all the heterogeneous
systems in terms of warning against accidents, weather, and
prediction of congestion. Vehicles sense the occurrence of
any non-usual event and send it to the other vehicles,
approaching that affected area using V2V communication.
When vehicles receive the warning message then it verifies
whether they are going through that area or not. If the area
is of interest for the vehicles, then using V2I
communication vehicles sends the request to the central
server for the computation of alternative routes. It controls
traffic congestion in both a proactive and reactive manner.
4.3.5 Efficient road congestion detection (ECODE)
protocol
A congestion detection system named ECODE has been
presented by Younes and Boukerche (2013). Two
assumptions made by ECODE are:
1 RSUs installed at every intersection
2 vehicles are communicating in multi-hop with nearby
vehicles by exchanging advertisement message that
contains the ID, speed, location, destination.
Table 5 Comparison of hybrid ITS
Models Congestion detection strategy Dissemination protocol Re-routing strategy Remarks
DIVERT Pan et al.
(2016)
Real-time data (Vehicle’s
speed and density)
Prioritised dissemination Load balancing
heuristic approach
Increases the user privacy
but also increases average
travel time
Guchhait and Debdatta
(2018)
Real-time data (Vehicle’s
speed and density)
Every vehicle
disseminates the local
knowledge
League championship
priority scheduling
High-speed transmission
but the installation cost is
high
ICARUS De Souza
et al. (2016)
Real-time data from the
vehicles within the area of
interest
Store and carry based Dijkstra’s k- shortest
path algorithm
A short delay and lower
overhead
ECODE Younes and
Boukerche (2013)
Multi-hop principle Store and carry based RecomReport
messages are used to
evaluate the distance
May overlap messages in
dense areas

234 S. Sharma and S.K. Awasthi
This model resolves the issue of the broadcast storm by
using the geocast principle of data dissemination. After
receiving the information from nearby vehicles each vehicle
aggregates the speed and flow. Further, the vehicle nearest
to RSU sends this information to the RSU. After receiving
the information RSUs check the database for the evaluation
for the best optimal path toward every destination. Then
RSU generates a RecomReport that has information of ID,
traffic monitoring report (TMR), best route, and total arrival
time towards the destination. The message received by
vehicle from the RSU is then transmitted to the neighbours
in a single hop. For switching purposes, relayed vehicles
have been used to avoid multiple transmissions.
Table 5 shows the comparison of various hybrid models
based on the strategy used for congestion detection,
dissemination protocol used for information exchange, and
the rerouting strategy used.
5 Qualitative analysis of ITS models
This section gives a brief analysis of the best ITS models in
terms of high coverage area, least overhead, congestion
detection accuracy, network efficiency, Broadcast storm,
and rerouting used by the ITS models.
5.1 Comparison among the ITS Models
Among all the ITS models, ECODE (Younes and
Boukerche, 2013) gives 100% detection of vehicles present
in the scenario. It is the first protocol that considered the
direction of the vehicle. Whereas other protocols consider
some vehicles from one direction and rest from the opposite
direction. ECODE gives the best results from the
perspective of communication overload and accuracy in
measurement.
From a data dissemination point of view, distributed ITS
models give better results as compared to the centralised
approach. CARRO (Akabane et al., 2016) is the one
distributed ITS model that selects the vehicles which are
located in a high priority area for data dissemination. It uses
a store-carry-forward concept that leads to delay in data
forwarding. It also increases the overhead as the vehicle
sends the information to the neighbouring vehicles until
they receive it. Faster (De Souza and Villas, 2016) uses the
number of message transmissions to create the global
knowledge of any district. This data dissemination leads to
communication overhead. EcoTrec (Doolan and Muntean,
2017) disseminates the information about the fuel
consumption and the ongoing road periodically. It uses the
Dijkstra algorithm for the calculation of a new route. This
model has no complete knowledge of traffic. This leads to
multiple overlapping of routes, congestion in the future, and
also the issue of scalability. CORONOS (Costa et al., 2017)
selects the vehicle nearest to the edge of the communication
radius as a relay node. This vehicle sends the information
with minimum overhead and minimum delay. Among all
dissemination protocols, the CORONOS gives tremendous
results in terms of transmission delay and overhead. dEASY
(Akabane et al., 2020) gives the least network overhead at a
higher density. ON demand (Gomides et al., 2020) gives the
best results in terms of geographical coverage. Hybrid
models are best in terms of coverage area. Divert (Pan
et al., 2016) has an issue in broadcast suppression and also
scalability. EcoTrec (Doolan and Muntean, 2017) gives a
good result in terms of area coverage as compared to
DIVERT, but at higher density these both models fail.
Researchers are working on hybrid ITS models for better
dissemination with minimum message transmission to avoid
network load.
Table 6 shows the comparison among various ITS
models based on architecture used, re-routing behaviour,
various challenges addressed, and their objectives.
5.2 Comparative analysis of protocols to address
broadcast storm issue
Data dissemination can be done by the fixed terminal or a
moving vehicle. Gathering any traffic information from the
non-line of sight location is not possible with the help of
sensors but is possible through vehicular communication.
Low latency and direct communication are the parameters
required for vehicular communication. Relaying packets are
required to transmit in VANET in a broadcast manner to
achieve this goal. Packet relaying overcomes the broadcast
storm issue. As these packets are primarily for a safety
perspective, so it is required to broadcast these packets to all
vehicles within the range rather than to a specific vehicle.
On reception of a single broadcast packet, all the receivers
relay that packet, which leads to packet collision also
known as a broadcast storm. To resolve this problem
various protocols are proposed by the researchers. There are
two types of relaying classes namely:
1 Delay-based relaying
2 Probability-based relaying.
Figure 11 shows the classification of various broadcast
storm protocols.
Figure 11 Classification of broadcast storm protocol
Data dissemination protocols
Probability based
approach
Delay-based approach
Floating Vehicle
approach
Infrastructure based
approach

Introduction to intelligent transportation system 235
Table 6 Comparison of various ITS models
Models Architecture Behaviour Network challenges Goals
COTEC Bauza and Gozalvez (2012) Distributed Proactive Broadcast storm/overload
control
Congestion detection
CARTIM Araujo et al. (2014) Distributed Proactive Broadcast storm Congestion detection
and avoidance
FASTER Akabane et al. (2020) Distributed Reactive Broadcast storm Congestion detection
and avoidance
UCONDES Meneguette et al. (2015) Distributed Proactive
Broadcast storm/overload
control
Congestion avoidance
GARUDA De Souza et al. (2015b) Distributed Proactive Broadcast storm Congestion detection
and avoidance
Eco driving Rakha and Kamalanathsharma
(2011)
Centralised Proactive Overload control Congestion avoidance
DTGDSS Le et al. (2012) Centralised Reactive Overload control Congestion avoidance
DSP Pan et al. (2012) Centralised Reactive Overload control Congestion avoidance
CHIMERA De Souza et al. (2016) Centralised Reactive Security/overload control Congestion avoidance
SCORPION De Souza et al. (2015a) Centralised Proactive Security/overload control Congestion detection
and avoidance
CARRO Akabane et al. (2016) Distributed Proactive Broadcast storm Congestion detection
and avoidance and
accident warning
DIVERT Pan et al. (2016) Hybrid Proactive Overload control Congestion detection
and avoidance
EcoTrec Doolan and Muntean (2017) Distributed Proactive Broadcast storm Congestion detection
and avoidance
RKSP Pan et al. (2012) Centralised Proactive Security Congestion detection
and avoidance
a-NRR Wang et al. (2016) Centralised Reactive Security Congestion detection,
avoidance, and accident
warning
Re-RouTE Guidoni et al. (2020) Centralised Reactive Security Congestion detection
and avoidance
FOX Brennand et al. (2019) Hybrid Proactive Broadcast storm Congestion detection
and avoidance
FOREVER Brennand et al. (2017) Hybrid Proactive Broadcast storm Congestion detection
and avoidance
PANDORA De Souza et al. (2017) Distributed Proactive
Broadcast storm/overload
control
Congestion detection
and avoidance
ON-DEMAND Gomides et al. (2020) Distributed Proactive + reactive Broadcast storm/knowledge
inefficiency/overload control
Congestion detection
and avoidance
dEASY Akabane et al. (2020) Distributed Proactive + reactive Broadcast storm Congestion detection
and avoidance
In the delay-based relaying approach particular delay is
assigned to the vehicles to broadcast the message. If the
relay vehicle receives the same message without any update,
then they will discard the relay packet. Farther first
(Martinez et al., 2011) relaying approach is one possible
solution that allocates the shorter waiting time to the
relaying vehicle that is farther from the transmitting vehicle.
Efficient directional broadcast (EDB) Li et al. (2007)
deploys the directional antenna for efficient relaying.
Whereas Oppcast (Li et al. 2011) uses the double phase
broadcast mechanism for high reliability and fast packet
relaying. Another relaying technique Nearest First has been
proposed in Kim et al. (2019) that transmits the CAM
messages to the nearest vehicles by reducing unnecessary
packet propagation. All the technique deploys DSRC
scheme for relaying operation whereas in Park et al. (2018)
author proposed a delay-based relaying technique that uses
LTE communication via RSUs.
In probability-based relaying probabilities are assigned
to the vehicles to become the relay node. The author
proposed a scheme in Tseng et al. (2002) that assigns the
same probability to all relaying vehicles. Another scheme
named slotted p-persistence broadcasting is proposed in
(Wisitpongphan et al., 2007) that allocates the higher

236 S. Sharma and S.K. Awasthi
probability to the relaying vehicle having the largest
distance from the transmitting vehicle. Autocast Wegener is
another scheme that falls under this category that assigns the
probability according to the vehicle’s nearby density.
Another type of approach is proposed by the author in
which relaying vehicle is selected based on degree centrality
Costa et al. (2018), which means a large number of vehicles
are in the range of that particular node. In Akabane et al.
(2020) author uses egocentric betweenness where the
vehicle is selected with the highest ego to relay the packet.
The issue with probability-based schemes is that they
are not good in the case of non-line-of-sight scenarios and
the road with more than one segment.
5.3 Comparison of centralised, distributed, and
hybrid ITS models in terms of rerouting
Services such as Google map, Apple map, and INIX
provides information to the commuter with some temporal
accuracy. Further with the help of this information
commuters choose the alternate path with minimum travel
time. Google maps can broadcast the congestion and the
duration of occurrence with the help of advanced statistical
predictive study of traffic scenarios. Various applications
utilise such information to convert smartphones into
navigation devices. But the problem with such services is
that they suggest the same path to every vehicle present in
the affected area that may lead to another local congestion.
This problem arises in centralised ITS models.
To overcome this problem researchers, suggest a
dynamic traffic assignment (DTA) approach which assigns
paths to every commuter that are either system optimal or
user optimal. These algorithms are comparatively slow to
provide information to the commuters regarding the
alternative paths to avoid congestion.
At present, DIVERT is the only hybrid approach that is
available to overcome the above issue but the problem with
that is it does not provide an optimal route in terms of
travelling time.
6 Key challenges in ITS development
This section presents the major challenges in the
development of ITS.
6.1 Integration of heterogeneous traffic data
ITS enables the integration of data coming from different
sources to improve the entire functionality. Still, it is an
open challenge for the researchers to make a single unit that
can establish the relationship between different sources and
units that provides traffic-related data. These units provide
the information without any standardisation. Integration of
data leads to other issues such as the monitoring of all the
units that are responsible for the integration. Security is
another issue as the devices send the knowledge regarding
the owner, this may suffer from the cyber-attacks. An
encryption technique is required for this purpose. This issue
is majorly faced by centralised ITS architecture.
6.2 Data management
ITS needs to manage a large amount of data. There are
issues of management and formatting with ITS, where the
various sources act independently to provide the data.
Management of huge asynchronous data is another issue.
Data correlation is not there due to the heterogeneity of data
provided by various sources. In this case, the same source
provides the information to other available sources due to
the lack of integration among the sources and systems. ITS
requires a mechanism for the fusion, aggregation, and
exploitation of data to deal with the data provided by the
heterogeneous sources. This is another major issue faced by
the centralised ITS.
6.3 Re-routing
Traffic efficiency can be improved by suggesting alternate
routes so that one can avoid congestion. Computation of
alternate routes within the desired time for the avoidance of
overhead and waiting at the traffic jam is an important issue.
Centralised ITS is efficient for this purpose because of its
management. Computation of the route for the number of
vehicles increases the complexity and overhead in the case
of the centralised system. This issue can be overcome by
individual route computation for the commuter, but it
requires complete traffic knowledge from source to
destination. Proactive re-routing is the basic need of ITS so
that one can decide in advance to avoid congestion. It leads
to system complexity and inefficiency.
6.4 Wi-Fi-based ITS vs DSRC based ITS
Wireless-Fidelity is an essential part of the society we are
living in. One cannot think of a place without Wi-Fi. DSRC
is an updated version of standard Wi-fi. There are two major
advantages of using the Wi-Fi-based ITS:
1 cost-efficient
2 Ease of implementation.
Wi-Fi uses the ISM band for its operation at 2.4GHz, which
is completely free. Whereas DSRC based ITS require the
adequate regulation of ISM band for the operation of OBU
and RSU’s. For the Wi-Fi-based ITS system to operate,
there is a requirement for a standard Wi-Fi router. In
contrast to this, in IEEE802.11p protocol based on the
protocol stack has the requirement of compatibility testing
and validation with OBUs and RSUs, which is cost
inefficient. Along with the advantages, the Wi-Fi-based ITS
comes with shortcomings. Throughput in urban areas is the
first issue with this system. According to the study, a single
access point (AP) is capable to handle five clients without
compromising throughput. Researchers are still working
with multiple AP but have not come up with tremendous
results till now. It is not possible to provide sufficient

Introduction to intelligent transportation system 237
bandwidth to every client with the help of multiple APs.
Whereas channel congestion is another issue. 2.4GHz band
is used by mobile devices, Wi-Fi, Bluetooth, etc., which
leads to high latency propagation. This problem is overcome
by DSRC as it operates at 5.8GHz–5.9GHz. Wi-Fi is best
suited for short-range V2I communication in rural areas.
6.5 Smartphones vs. OBUs and RSUs
Smartphones play a vital role in the present era.
Smartphones come up with inbuilt sensors like GPS,
odometer, Inertial Measurement Units, etc. With the help of
smartphones, the commuter can send and receive
information to and from the neighbouring vehicles
respectively. As it does not require expensive IEEE802.11p
based OBUs and RSUs for this purpose. LTE infrastructure
is used to provide a high bandwidth network. The
smartphone can be used for both V2V and V2I
communication. Issues with the smartphone based ITS are
the requirement of low latency and higher bandwidth as it
uses highly dynamic topology. This is not possible by using
the 3G network. Data rate slows down with the increase in
the number of devices (smartphones) using the same
cellular network. Security is another concern with
smartphone based ITS. This is another challenge that
researchers are facing nowadays. Since OBUs and RSUs are
there only in a few vehicles because of their high cost.
7 Conclusions
ITS functionality majorly suffers due to the variable speed
of vehicles, dynamic topology as it employs ad-hoc
network, sparse and dense traffic distribution, and
bandwidth limitation in arterial scenarios that lead to delay
and interference in communication. These issues create
interruptions in real-time decision-making, which
consequently leads to traffic congestion and collision
occurrence. We studied various kinds of ITS models based
on the physical architecture that is broadly classified into
three categories, centralised, distributed, and hybrid.
 In centralised or traditional approach vehicles send the
information to the central entity, and the central entity
processes the data and broadcast the data to the
vehicles.
 The scalability and security are the major issues with a
centralised system in which vehicles have to send and
receive data from the central server. It is inefficient as
its performance is limited by line-of-sight scenario. It is
very difficult for the server to handle and compute such
big data. Also, this technique is not secure as vehicles
have to share their origin and destination with the
central entity.
 To overcome the issues faced by the centralised ITS
model infrastructure-less model is proposed by the
various author named distributed ITS.
 The distributed ITS model uses V2V communication.
The issues with this model are less coverage range and
broadcast storms because vehicles are sending and
receiving information at the same time from all
neighbouring vehicles. It uses probabilistic and delay-
based approaches to address the problem but is not
suitable when it comes to the road having more than
one segment.
 The best approach among these models is hybrid.
Because of roadside infrastructure, coverage range
increases by multi-hopping which makes the vehicle
send and receive data in advance as all the RSUs are
connected through the internet.
 In the hybrid approach, it is difficult to select the
vehicle for data dissemination due to the highly mobile
scenario. In terms of routing, it is not able to provide
the optimal solution to avoid congestion.
 Although the hybrid model is the best approach for the
utilisation of available infrastructure, still there is a
need to explore it in more detail for the best
management of traffic flow.
References
Akabane, A.T., Immich, R., Bittencourt, L.F., Madeira, L.F. and
Villas, E.R.M. (2020) ‘Towards a distributed and
infrastructure-less vehicular traffic management system’,
Comput. Commun., 10 February, Vol. 159, pp.317–330,
Elsevier [online] https://www.sciencedirect.com/journal/
computer-communications/issues.
Akabane, A.T., Pazzi, R.W., Madeira, E.R.M. and Villas, L.A.
(2016) ‘CARRO: a context-Awareness protocol for data
dissemination in urban and highway scenarios’, in 8th IEEE
Latin-American Conference on Communications,
LATINCOM, pp.1–5, DOI:10.1109/LATINCOM.2016.
7811593.
Ali, Q.E., Ahmad, N., Malik, A.H., Ali, G. and ur Rehman, W.
(2018) ‘Issues, challenges, and research opportunities in
intelligent transport system for security and privacy’, Appl.
Sci., Vol. 8, No. 10, pp.1–24, DOI:10.3390/app8101964.
Al-Sultan, S., Al-Doori, M.M., Al-Bayatti, A.H. and Zedan, H.
(2014) ‘A comprehensive survey on vehicular ad hoc
network’, J. Netw. Comput. Appl., Vol. 37, No. 1,
pp.380–392, DOI: 10.1016/j.jnca.2013.02.036.
An, S., Lee, B-H. and Shin, D-R. (2011) ‘A survey of intelligent
transportation systems’, in 3rd International Conference on
Computational Intelligence, Communication Systems and
Networks, pp.332–337.
Araujo, G.B., Queiroz, M.M., Duarte-Figueiredo, F.de.L.P. and
Tostes, A.I.J. (2014) ‘CARTIM: a proposal toward
identification and minimization of vehicular traffic congestion
for VANET’, in IEEE Symposium on Computer and
Communication (ISCC), pp.1–6.
Arena, F., Pau, G. and Severino, A. (2020) ‘Review on
IEEE802.11p for intelligent transportation systems’, J. Sens.
Actuator Networks, Vol. 9, No. 2, pp.22–32.

238 S. Sharma and S.K. Awasthi
Baiocchi, A., Cuomo, F., De Felice, M. and Fusco, G. (2015)
‘Vehicular ad-hoc networks sampling protocols for traffic
monitoring and incident detection in Intelligent
Transportation Systems’, Transp. Res. Part C Emerg.
Technol., Vol. 56, pp.177–194, DOI: 10.1016/j.trc.2015.
03.041.
Barba, C.T., Mateos, M.Á., Soto, P.R., Mezher, A.M. and Igartua,
M.A. (2012) ‘Smart city for VANETs using warning
messages, traffic statistics and intelligent traffic lights’, in
IEEE Intelligent Vehicles Symposium (IV), pp.902–907,
DOI:10.1109/IVS.2012.6232229.
Bauza, R. and Gozalvez, J. (2012) ‘Traffic congestion detection in
large-scale scenarios using vehicle-to-vehicle
communications’, J. Netw. Comput. Appl., Vol. 36, No. 5,
pp.1–13, DOI: 10.1016/j.jnca.2012.02.007.
Board, T.R. (2016) ‘Highway capacity manual – a guide for
multimodal mobility analysis’ [online] https://www.trb.org/
publications/hcm6e.aspx.
Bouassida, M.S. (2011) ‘Authentication vs. privacy within
vehicular ad hoc networks’, Int. J., Vol. 12, No. 3,
pp.259–272.
Brennand, C., Filho, G.P.R., Maia, G. and Felipe, C.D. (2019)
‘Towards a fog-enabled intelligent transportation system to
reduce traffic jam’, Sens., Vol. 19, No. 18, p.3916.
Brennand, C.A.R.L., Boukerche, A., Meneguette, R. and Villas,
L.A. (2017) ‘A novel urban traffic management mechanism
based on FOG’, in Proceedings - IEEE Symposium on
Computers and Communications, pp.377–382,
DOI:10.1109/ISCC.2017.8024559.
Celso, B., De Souza, A.M. and Maia, G. (2015) ‘An intelligent
transportation system for detection and control of congested
roads in urban centers’, in 20th IEEE symposium on
computers and communication (ISCC), pp.476–481.
Costa, J., Lobato, W., De Souza, A.M., Rosario, D., Villas, L.A.
and Cerqueira E. (2017) Centrality-based data dissemination
protocol for vehicular ad hoc networks’, in IEEE 16th
International Symposium on Network Computing and
Applications, NCA, January, pp.1–4, DOI:10.1109/NCA.
2017.8171333.
Costa, J., Ros, D., De Souza, A.M., Villas, L.A. and Cerqueira, E.
(2018) ‘Data dissemination based on complex networks,
metrics for distributed traffic management systems’, in IEEE
Symposium on Computers and Communications (ISCC) Data,
pp.1062–1067.
De Souza, A.M. and Villas, L.A. (2016) ‘A fully-distributed traffic
management system to improve the overall traffic efficiency’,
in Conference on Modeling, Analysis and Simulation of
Wireless and Mobile Systems (ACM), pp.19–26.
De Souza, A.M., Botega, L.C. and Villas, L. (2018) FnS :
enhancing traffic mobility and public safety based on a hybrid
transportation system’, in 14th International Conference on
Distributed Computing in Sensor Systems (DCOSS),
pp.77–84, DOI:10.1109/DCOSS.2018.00018.
De Souza, A.M., Braun, T., Botega, L.C., Cabral, R. and Garcia,
I.C. (2019) ‘Better safe than sorry: a vehicular traffic re-
routing based on traffic conditions and public safety issues’,
J. Internet Serv. Appl., Vol. 9, No. 10, pp.1–17.
De Souza, A.M., Brennand, C.A.R.L. Yokoyama, R.S., Donato,
E.A., Madeira, E.R.M. and Villas, L.A. (2017a) ‘Traffic
management systems: A classification, review, challenges,
and future perspectives’, Int. J. Distrib. Sens. Networks,
Vol. 13, No. 4, pp.1–14, DOI: 10.1177/1550147716683612.
De Souza, A.M., Fonseca, N.L.S. and Villas, L. (2017b) ‘A fully-
distributed advanced traffic management system based on
opportunistic content sharing’, in IEEE International
Conference on Communications (ICC), pp.1–6.
De Souza, A.M., Maia, G., Braun, T. and Villas, L.A. (2019) ‘An
interest-based approach for reducing network contentions in
vehicular transportation systems’, Sensors (Switzerland),
Vol. 19, No. 10, pp.1–21, DOI:10.3390/s19102325.
De Souza, A.M., Yokoyama, R.S., Azzedine, B. and Maia, G.
(2016) ‘Icarus: improvement of traffic condition
through an alerting and re-routing system’, Comput.
Networks, 9 December, Vol. 110, pp.118–132 [online]
https://www.sciencedirect.com/journal/computer-
networks/issues.
De Souza, A.M., Yokoyama, R.S., Botega, L.C., Meneguette, R.I.
and Villas, L.A. (2015a) ‘SCORPION: a solution using
cooperative rerouting to prevent congestion and improve
traffic condition’, in Proceedings-15th IEEE International
Conference on Computer and Information Technology, CIT
2015, 14th IEEE International Conference on Ubiquitous
Computing and Communications, IUCC 2015, 13th IEEE
International Conference on Dependable, Autonomic and Se,
pp.497–503, DOI:10.1109/CIT/IUCC/DASC/PICOM.2015.
71.
De Souza, A.M., Yokoyama, R.S., Fonseca, N.L.S., Meneguette,
R.I. and Villas, L.A. (2015b) ‘GARUDA: a new geographical
accident aware solution to reduce urban congestion’, in IEEE
International Conference on Computer and Information
Technology; Ubiquitous Computing and Communications;
Dependable, Autonomic and Secure Computing;
Pervasive Intelligence and Computing, pp.596–602,
DOI:10.1109/CIT/IUCC/DASC/PICOM.2015.85.
De Souza, A.M., Yokoyama, R.S., Maia, G., Loureiro, A. and
Villas, L. (2016) ‘Real-time path planning to prevent traffic
jam through an intelligent transportation system’, in
Proceedings - IEEE Symposium on Computers and
Communications, August, pp.726–731, DOI:10.1109/ISCC.
2016.7543822.
Doolan, R. and Muntean. G.M. (2017) ‘EcoTrec-A novel Vanet-
based approach to reducing vehicle emissions’, IEEE Trans.
Intell. Transp. Syst., Vol. 18, No. 3, pp.608–620, DOI:10.
1109/TITS.2016.2585925.
Ezell, S. (2010) Explaining International IT Applications
Leadership: Intelligent Transportation Systems [online]
https://trid.trb.org/view/911843.
Feng, S. and Haykin, S. (2019) ‘Cognitive risk control for anti-
jamming V2V communications in autonomous vehicle
networks’, IEEE Trans. Veh. Technol., Vol. 68, No. 10,
pp.9920–9934.
Gomides, T.S., De Grande, R.E., de. Souza, A.M., Souza, F.S.H.,
Villas, L.A. and Guidoni, D.L. (2020) ‘An adaptive and
distributed traffic management system using vehicular ad-hoc
networks’, Comput. Commun., 1 June, Vol. 159, pp.317–330,
Elsevier [online] https://www.sciencedirect.com/journal/
computer-communications/issues.
GPS.gov: GPS Overview https://www.gps.gov/systems/gps/
(accessed 20 July 2021).
Guchhait, A.M.B, and Debdatta, K. (2018) ‘A hybrid V2V system
for collision-free high-speed internet access in intelligent
transportation system’, Trans. Emerg. Telecommun. Technol.,
Vol. 29, No. 3, pp.1–9.

Introduction to intelligent transportation system 239
Guidoni, D.L., Maia, G., Souza, F.S., Villas, L.A. and
Loureiro, A.A.F. (2020) ‘Vehicular traffic management based
on traffic engineering for vehicular ad hoc networks’, IEEE
Access, vol. 8, pp.45167–45183, 2020.
Khan, S.M., Rahman, M., Apon, A. and Chowdhury, M. (2017)
Chapter 1. Characteristics of Intelligent Transportation
Systems and Its Relationship With Data Analytics,
Elsevier Inc. [online] https://www.sciencedirect.com/book/
9780128097151/data-analytics-for-intelligent-transportation-
systems.
Kim, B. et al. (2019) ‘Nearest-first: Efficient relaying scheme in
heterogeneous V2V communication environments’, IEEE
Access, vol. 7, pp.23615–23627 DOI: 10.1109/ACCESS.
2019.2899111.
Korablev, V., Gugutishvili, D. and Lepekhin, B. (2021)
‘Aleksandr, gerrits. developing a traffic management system
architecture model’, Transp. Res. Procedia, Vol. 54,
pp.918–926 [online] https://www.sciencedirect.com/journal/
transportation-research-procedia/issues.
Lee, W.H., Lai, Y.C. and Chen, P.Y. (2012) ‘Decision-tree based
green driving suggestion system for carbon emission
reduction’, in 12th International Conference on ITS
Telecommunications, ITST, pp.486–491, DOI:10.1109/ITST.
2012.6425226.
Li, D., Huang, H., Li, X., Li, M. and Tang, F. (2007) ‘A distance-
based directional broadcast protocol for urban vehicular ad
hoc network’, Int. Conf. Wirel. Commun. Netw. Mob.
Comput., WiCOM, pp.1520–1523, DOI:10.1109/WICOM.
2007.383.
Li, M., Zeng, K. and Lou, W. (2011) ‘Opportunistic broadcast of
event-driven warning messages in Vehicular Ad Hoc
Networks with lossy links’, Comput. Networks, Vol. 55,
No. 10, pp.2443–2464, DOI: 10.1016/j.comnet.2011.04.005.
Li, Y., Ouyang, K., Li, N., Rahmani, R., Yang, H. and Pei, Y.
(2020) ‘A blockchain-assisted intelligent transportation
system promoting data services with privacy protection’,
Sensors (Switzerland), Vol. 20, No. 9, p.2483,
DOI:10.3390/s20092483.
Lu, N., Cheng, N., Zhang, N., Shen, X. and Mark, J.W. (2014)
‘Connected vehicles: Solutions and challenges’, IEEE
Internet Things J., Vol. 1, No. 4, pp.289–299, DOI:10.1109/
JIOT.2014.2327587.
Lyamin, N., Kleyko, D., Quentin, D. and Vinel, A. (2019) ‘Real-
time jamming DoS detection in safety-critical V2V C-ITS
using data mining’, IEEE Commun. Lett., Vol. 33, No. 3,
pp.442–445.
Mandžuka, S., Žura, M., Horvat, B., Bićanić, D. and Mitsakis, E.
(2013) ‘Directives of the European union on intelligent
transport systems and their impact on the republic of Croatia’,
Promet - Traffic - Traffico, Vol. 25, No. 3, pp.273–283,
DOI:10.7307/ptt.v 25i3.1323.
Martinez, F.J., Toh, C.K., Cano, J.C., Calafate, C.T. and Manzoni,
P. (2011) ‘A street broadcast reduction scheme (SBR) to
mitigate the broadcast storm problem in VANETs’, Wirel.
Pers. Commun., Vol. 56, No. 3, pp.559–572, DOI: 10.1007/
s11277-010-9989-4.
Meneguette, R.I., Filho, G.P.R., Bittencourt, L.F., Ueyama, J.,
Krishnamachari, B. and Villas, L.A. (2015) ‘Enhancing
intelligence in inter-vehicle communications to detect and
reduce congestion in urban centers’, in Proceedings - IEEE
Symposium on Computers and Communications (ISCC),
2015, pp.1–6, DOI:10.1109/ISCC.2015.8897528.
Mollah, M.B. et al., (2021) ‘Blockchain for the internet of vehicles
towards intelligent transportation systems: a survey’, IEEE
Internet Things J., Vol. 8, No. 6, pp.4157–4185.
Navas, F. and Milanes, V. (2019) ‘Mixing V2V- and non-V2V-
equipped vehicles in car following’, Transp. Res. Part C
Emerg. Technol. November, Vol. 108, pp.167–181,
Elsevier, [online] https://www.sciencedirect.com/journal/
transportation-research-part-c-emerging-technologies/issues.
Pan, J., Khan, M.A., Popa, I.S., Zeitouni, K. and Borcea, C. (2012)
‘Proactive vehicle re-routing strategies for congestion
avoidance’, in Proceedings - IEEE International Conference
on Distributed Computing in Sensor Systems, DCOSS
pp.265–272, DOI:10.1109/DCOSS.2012.29.
Pan, J., Popa, I.S. and Borcea, C. (2016) ‘Divert: A distributed
vehicular traffic re-routing system for congestion avoidance’,
IEEE Trans. Mob. Comput., Vol. 16, No. 1, pp.58–72.
Park, S., Kim, B., Yoon, H. and Choi, S. (2018) ‘RA-eV2V:
relaying systems for LTE-V2V communications’,
J. Commun. Networks, Vol. 20, No. 4, pp.396–405,
DOI:10.1109/JCN.2018.000055.
Rakha, H. and Kamalanathsharma,, R.K. (2011) ‘Eco-driving at
signalized intersections using V2I communication’, in IEEE
Conference on Intelligent Transportation Systems,
Proceedings, ITSC, pp.341–346, DOI:10.1109/ITSC.
2011.6083084.
Rizzo, G., Ajmone, M., Braun, T. and Manzo, G. (2011) ‘Optimal
strategies for floating anchored information with partial
infrastructure support’, VEH. Commun., Vol. 27, p.87, DOI:
10.1016/j.vehcom.2020.100287.
Shen, X., Cheng, X., Yang, L., Zhang, R. and Jiao, B. (2014) ‘Data
dissemination in VANETs: a scheduling approach’, IEEE
Trans. Intell. Transp. Syst., Vol. 15, No. 5,
pp.2213–2223, 2014, DOI:10.1109/TITS.2014.2313631.
Souza, A.M., Maia, G. and Villas, L.A. (2014) ‘ADD: a data
dissemination solution for highly dynamic highway
environments’, in IEEE 13th International Symposium on
Network Computing and Applications (NCA), pp.17–23.
Sun, G., Zhang, Y., Yu, H., Du, X. and Guizani, M. (2020)
Intersection fog-based distributed routing for V2V
communication in urban vehicular ad hoc networks’, IEEE
Trans. Intell. Transp. Syst., Vol. 21, No. 6, pp.2409–2426.
Tangade, S.S. and Manvi, S.S. (2013) ‘A survey on attacks,
security and trust management solutions in VANETs’, 4th Int.
Conf. Comput. Commun. Netw. Technol, ICCCNT, pp.2–7,
DOI:10.1109/ICCCNT.2013.6726668.
Trien, L.T., Adachi, K. and Yamao, Y. (2018a) Efficient
CSMA/CA packet relay- assisted scheme with payload
combining for ITS V2V communication’, J. Inf. Process.,
Vol. 26, pp.11–19.
Trien, L.T., Adachi, K. and Yamao, Y. (2018b) Packet
relay-assisted V2V communication with sectorised relay
station employing payload combining scheme’, IET Intell.
Transp. Syst., Vol. 12, No. 4, pp.458–465.
Tseng, Y-C., Ni, S-Y., Chen, Y-S. and Sheu, J-P. (2002) ‘The
broadcast storm problem in a mobile ad hoc network’,
Wireless Networks, Vol. 8, No. 2, pp.153–167.
USDOT (2019) Intelligent Transportation Systems (ITS) Strategic
Plan 2015–2019 [online] https://www.its.dot.gov/factsheets/
itsjpo_stratplan.htm (accessed 7 November 2021).

240 S. Sharma and S.K. Awasthi
Villas, L.A., De Andrade, T.P.C. and Da Fonseca, N.L.S. (2014)
An efficient and robust protocol to disseminate data in
highway environments with different traffic conditions’, in
Proceedings - IEEE Symposium on Computers and
Communications, pp.1–6, DOI:10.1109/ISCC.2014.6912605.
Wang, R., Xu, Z., Zhao, X. and Hu, J. (2019) ‘V2V-based method
for the detection of road traffic congestion’, IET Intell.
Transp. Syst., Vol. 13, No. 5, pp.880–885.
Wang, S. Djahel, S. and McManis, J. (2016) ‘An adaptive and
VANETs-based next road re-routing system for unexpected
urban traffic congestion avoidance’, in IEEE Vehicular
Networking Conference, VNC, January, pp.196–203,
DOI:10.1109/VNC.2015.7385577.
Wegener, A., Hellbrück, H., Fischer, S., Schmidt, C. and Fekete,
S. (2007) ‘AutoCast: An adaptive data dissemination protocol
for traffic information systems’, IEEE Veh. Technol. Conf.,
pp.1947–1951, 2007, DOI:10.1109/VETECF.2007.409.
Wischhof, L., Ebner, A., Rohling, H., Lott, M. and Halfmann, R.
(2003) ‘SOTIS - A self-organizing traffic information
system’, in IEEE Vehicular Technology Conference, Vol. 57,
No. 4, pp.2442–2446, DOI: 10.1109/vetecs.2003.1208829.
Wisitpongphan, N., Tonguz, O.K., Parikh, J.S., Mudalige, P., Bai,
F. and Sadekar, V. (2007) ‘Broadcast storm mitigation
techniques in vehicular ad hoc networks’, IEEE
Wirel. Commun., Vol. 14, No. 6, pp.84–94,
DOI:10.1109/MWC.2007.4407231.
Yang, X. et al. (2015a) ‘Towards a low-cost remote memory
attestation for the smart grid’, Sensors, Vol. 15,
pp.20799–20824, DOI: 10.3390/s150820799.
Yang, X., Lin, J., Yu, W., Moulema, P.M., Fu, X. and Zhao, W.
(2015b) ‘A novel en-route filtering scheme against false data
injection attacks in cyber-physical networked systems’, IEEE
Trans. Comput., Vol. 64, No. 1, pp.4–18, DOI:10.1109/TC.
2013.177.
Younes, M.B. and Boukerche, A. (2013) ‘Efficient traffic
congestion detection protocol for next generation VANETs’,
in IEEE International Conference on Communications,
pp.3764–3768, DOI:10.1109/ICC.2013.6655141.
Zhang, J. (2011) ‘A survey on trust management for VANETs’,
Proc. – Int. Conf. Adv. Inf. Netw. Appl., AINA, pp.105–112,
DOI:10.1109/AINA.2011.86.
Zhao, N., Yu, F.R., Li, M. and Leung, V.C.M. (2016) ‘Anti-
eavesdropping schemes for interference alignment (IA)-based
wireless networks’, IEEE Trans. Wirel. Commun., Vol. 15,
No. 8, pp.5719–5732, DOI:10.1109/TWC.2016.2568188.