Implementation of AI in Traffic Management: Need, Current Techniques and Challenges

Abstract

In the most recent years, technology, including Artificial Intelligence (AI) has advanced in every industry conceivable. Artificial Intelligence has been a keen area of interest in solving everyday problems that humans face. One such area of application is Traffic Management, which required extensive human involvement since the beginning, but now researchers from all over the world are working to introduce the use of Artificial Intelligence in managing the road traffic in the major cities to eliminate the issue of traffic congestion. Artificial Intelligence (AI) has emerged as a promising solution for addressing the persistent challenges associated with traffic management in major cities. Several attempts have been made in this regard and different techniques are being worked upon till date, but the world is still far from observing the widespread use of AI in major cities. This paper presents an analysis on why Artificial Intelligence is needed for traffic management, what techniques have been developed so far and what are the common challenges that are hindering the widespread application of AI in traffic management.

Full text

Pakistan Journal of Scientific Research, PJOSR
ISSN (p): 0552-9050
Volume: 3, Number: 1, Pages: 20- 25, Year: 2023
This work is licensed under a Creative Commons Attribution –Strike Alike 4.0 International
License, which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly cited.
20
20
Implementation of AI in Traffic Management: Need,
Current Techniques and Challenges
S.Faiza Nasim, Asma Qaiser, Nazia Abrar and Umme Kulsoom
NED University of Engineering and Technology, Pakistan
Corresponding authors: S.Faiza Nasim (e-mail: sfaizaadnan@gmail.com)
Abstract—In recent years, technology, including Artificial Intelligence (AI), has advanced in every industry conceivable. Artificial
Intelligence has been a keen area of interest in solving everyday human problems. One such application area is Traffic Management,
which has required extensive human involvement since the beginning. Still, researchers from all over the world are working to
introduce the use of Artificial Intelligence in managing road traffic in major cities to eliminate the issue of traffic congestion. Artificial
Intelligence (AI) has emerged as a promising solution for addressing the persistent challenges of traffic management in major cities.
Several attempts have been made in this regard, and different techniques are being worked upon to date, but the world is still far from
observing the widespread use of AI in major cities. This paper analyzes why Artificial Intelligence is needed for traffic management,
what techniques have been developed so far, and the common challenges hindering the widespread application of AI in traffic
management.
Index Terms—Artificial Intelligence (AI), Blockchain, Internet of Things (IoT), Traffic Congestion, Machine Learning (ML) and
Neural Network.
I. INTRODUCTION
With the advent of the 21st century, Artificial Intelligence has
been in the spotlight. Many technical personnel from all fields
have been trying to implement AI in nearly every walk of life
to improve human living conditions [1-5]. One major area of
interest in this regard is Traffic Management.
With the huge spike in population, globalization, and
advancement in automobile technology, more people are
opting to have their own car, creating traffic congestion issues
mainly in metropolitan cities. Urban regions have always
struggled with traffic congestion [6], which occurs when
demand exceeds available road space. Traffic congestion is
one of the biggest problems that the world is facing right now,
which is worsening every day with the increase in the number
of vehicles on the roads [7]. This brings upon itself a plethora
of related issues like air pollution [8], sound pollution [9],
stress on city infrastructure, reduction in overall productivity,
energy wastage in terms of fossil fuels [10], and mental stress
across the city population. So efficient traffic management is
the need of the hour and Artificial Intelligence can be very
helpful in this matter to manage the traffic management
infrastructure with minimal human involvement [11].
Considering the severity of the traffic congestion issue,
researchers and engineers from all over the world have
proposed different working models and systems which can be
installed in. the city Centre to tackle this issue[12,13].
Different techniques are being considered to this day and few
commercial solutions are also available in the market from
different solution providers [14, 15].
Furthermore, [24] suggested that the greater rates of growth in
population, urbanization, and changes in density of population
are factors that result in difficulties for the network of roads in
comparison with the requirements for effective movement of
people and goods, as well as the creation of a viable
transportation network in South African cities. These problems
not only delay city infrastructure development but also cause
automobile accidents, traffic congestion, and an increase in
journey times, fuel consumption, and carbon emissions.
However, congestion in traffic is seen as a significant issue
that impedes safe street transit in South African cities. While a
few traffic-control initiatives have been implemented, such as
the Gautrain and the Bus Rapid Transit System (BRT) in
Pretoria and Johannesburg, and the MyCiTi move structure in
Cape Town, certain regions of the urban areas continue to face
transportation challenges. During the peak hours of the day,
metropolitan areas with no public transit framework may
experience significant traffic congestion. Hence, there's an
obligation to examine alternate roadways that might alleviate
Johannesburg's traffic congestion. As a result, it is believed
that there's a need to examine alternate roadways that might
alleviate Johannesburg's traffic congestion.
A lot of investigation has been done on the use of AI in
transportation systems in recent years, especially on how it
may be used to anticipate traffic flow on roads and its
applicability in alleviating traffic congestion. However, little
progress has been made in areas of reducing congestion at
signalized road junctions, particularly when the vehicles at
these crossings are autonomous cars [24].
Autonomous cars are the years to come of road travel, with
solid data indicating that they will help decrease congestion on
the roads by a minimum of 50%. It will also help to eliminate
or reduce the use of gap approval theory in un-signalized
roadway intersections, as well as reduce major road accidents

21
because autonomous vehicles can react faster than non-
autonomous vehicles in hazardous situations due to their
computational intelligence.
According to extant literature evaluations, the application of
artificial neural networks (ANN) in traffic flow prediction is
not innovative. Previous related research, however, ignored the
use of artificial intelligence in the domain of road junctions,
particularly signalized road crossings. This conference paper
attempted to shed as much as possible light on this element of
road intersections. This study is primarily concerned with:
Signalized Road Intersections. Using Artificial Neural
Networks to Reduce Traffic Congestion at Signalized Road
Intersection
But the fact that requires our attention is that irrespective of all
the development and advancement in the field of AI and its
use with electronic control systems to solve complex
problems, especially in the area of traffic management, World
is still very far to see widespread deployment of such systems
in the practical world. Although there are many reasons behind
this, core issues are the capital investment required to design
and implement such systems, the need for prior infrastructure,
complexity, and ethical issues. However, it is important to note
that the implementation of AI in traffic management must be
done responsibly and with consideration for potential ethical
and privacy concerns [22].
II. LITERATURE REVIEW
This section highlights the designs and principles of working
on the different AI-based traffic management systems
proposed by different creators and researchers.
● Huang et al designed an AI-based traffic management
system based on the Neural Network technique called YOLO
[16]. This system collects data from CCTV[20] cameras
installed on the roads and processes it through an algorithm to
calculate traffic density by categorizing each class of vehicle,
such as car, bike, bus, etc. Then this system decides which
traffic signal needs to be turned green and for which duration
based on the traffic density on the road.
● One of the most important study in traffic management
system is proposed by uddin et al. Their research based on an
AI-based traffic management system with added IoT
functionality [18]. This system also uses a camera to detect the
real-time traffic situation on the road and then process this
data with the NVIDIA ML algorithm to calculate the traffic
density and turn the traffic signal on/off accordingly, also the
traffic violating vehicle can also be detected and the traffic
regulating authorities can be informed about the offender and
a text message will also be sent to the offender via the MQTT.
[15]
● Soman et al proposed a digital image processing based
smart traffic management system that uses CCTV cameras to
collect data and then calculate traffic density at intersections
with the help of “openCV” [19] platform. This helps in
deciding the timings for different traffic signals.
● Gandhi et al proposed a traffic management based on AI
& image processing. A single pole-mounted camera rotates
and takes the image of the road periodically to gather data
[17]. Then these images are converted to gray scale and
techniques of canny edge detection and image enhancement
are used to compare the taken image with the reference image
of the empty road to calculate the timer values of traffic signal
with the greater traffic density. This system also distinguishes
the lane with ambulances or fire safety vehicles via sound
sensor [18] and liberates that lane on priority.
● Sharma et al presented a comprehensive, integrated
Smart Road Traffic Management System (SRTMS). This
paper suggests integrating different sensors in the vehicle from
the factory to detect driving patterns and habits. The traffic
situation is determined by the communication of the vehicle
with different objects on the road, for example, poles, traffic
lights [17], other vehicles, etc. All the communications will be
done via blockchain [21] to minimize the data breach. The
whole system will be able to manage traffic efficiently, detect
accidents on the road, and inform the traffic authorities about
an incident and offender.
● Degas et al presented the use of artificial intelligence
approaches to urban traffic management problems in order to
improve the performance of present signal design selection
systems. In specifically, the architecture of a smart traffic
management system is described in terms of the many layers
of gathering information, the interpretation and analysis of
data, decision, and control. The features of the presented
hybrid modules are explained, as are the artificial intelligence
technologies applied. Finally, current research in the topic is
described [25].
● Iyer et al in their research, examine the uses of AI in the
sphere of public transportation on the path to developing a
sustainable society. For this analysis, four subsystems of the
Intelligent Transportation System are considered: traffic
management, public transportation, safety management,
manufacturing, and logistics. In addition, the research
aggregated several AI applications from various cities and
organizations and provided them with a point of reference for
future leaders [26]. Their study will help the industrial and
transportation industries discover innovative ways to use AI as
an answer in their respective fields. Prior to implementation,
businesses might weigh the benefits and drawbacks of the
proposals and take a careful step toward creating a sustainable
society [26].
● Sukhadia et al proposed, a smart congestion governance
system uses artificial intelligence to oversee and regulate the
course of transportation, as well as automated administration
and execution, to make a difference in the face of travel
scenarios in large centers with substantial traffic concerns
[27].
● Okwu et al describes yet another AI approach for
predicting traffic flow [28]. Using Jordan's neural network, the
authors created a basic recurrent neural network that can be
used for short-term forecasting. The inclusion of a context
layer distinguishes it from traditional feedforward networks.
The context layer functions as a memory box, storing prior
information. The data that has been saved at (t-1) is then sent
back into the hidden layer together with the input at (t). This
assists the network in predicting the subsequence; as a result,
it is frequently referred to as "Jordan's Sequential Network."
The input data was traffic volume from Ireland's road traffic
control. The outcome is the predicted traffic flow to reduce

22
error, the network in question undergoes training as a network
of feed-forward neural networks with a back propagation
technique. But the scientists demonstrated that the network
● performs better when the total amount of neurons in the
layer that is hidden is double that of the input neurons. Also,
using a rate of learning of a value of and a decreasing number
of iterations, flow accuracy is predicted to be between 92 and
98%. Because this model is a first order system, it offers
erroneous predictions while computing higher order dynamics.
[29] It was recommended that the network be linearized often
at each operational point live to make algorithm calculation
easier. Elman Network is another sort of recurrent network.
ATOS Academic Community also created a Pattern-Based
Strategy (PBS). Supervised learning, whereby labelled data is
utilized with a proper output, is the initial type of recognition
of patterns. Second, unsupervised learning uses unlabeled data
to locate a pattern and determine the proper output; and semi-
supervised learning uses little-supervised data with a large
amount of unlabeled data for pattern recognition analysis. [30]
Similarly, modern rideshare services like Uber and Didi
Chuxing, among others, have raised the possibilities of
enormous data collection. AI can use this data to efficiently
estimate passenger demand, avoiding empty cars and therefore
reducing congestion and energy usage [31].
● Luo et al proposes a deep learning model that takes into
account Multi-View, Spatial, and Temporal (DMVST)
Network. The authors gathered large-scale ridesharing demand
information from DiDi Chuxing in the Chinese city of
Guangzhou. They used a combination of Local CNN, which
captures local areas in relation to their surroundings, and a
long-short-term memory networks (LSTM), which models
temporal aspects.The results showed that the proposed model
outperformed others [31].
● Similarly, Xu, Ying et al used a Multi-layer Perceptron
Neural Network to forecast taxi demand in Tokyo, Japan.
They gather data from the Taxi Probe system, in which cabs
are outfitted with sensors that capture information (e.g., taxi
location) [32]. The results revealed that using 4 hours of
historical demand data with fifty neurons in the layers that
were concealed improved forecast accuracy. distinguishes it
from traditional feedforward networks. The context layer
functions as a memory box, storing prior information. The
data that has been saved at (t-1) is then sent back into the
hidden layer together with the input at (t). This assists the
network in predicting the subsequence; as a result, it is
frequently referred to as "Jordan's Sequential Network." The
input data was traffic volume from Ireland's road traffic
control. The outcome is the predicted traffic flow to reduce
error, the network in question undergoes training as a network
of feed-forward neural networks with a back propagation
technique. But the scientists demonstrated that the network
performs better when the total amount of neurons in the layer
that is hidden is double that of the input neurons. Also, using a
rate of learning of a value of and a decreasing number of
iterations, flow accuracy is predicted to be between 92 and
98%. Because this model is a first order system, it offers
erroneous predictions while computing higher order dynamics.
[29] It was recommended that the network be linearized often
at each operational point live to make algorithm calculation
easier. Elman Network is another sort of recurrent network.
ATOS Academic Community also created a Pattern-Based
Strategy (PBS). Supervised learning, whereby labelled data is
utilized with a proper output, is the initial type of recognition
of patterns. Second, unsupervised learning uses unlabeled data
to locate a pattern and determine the proper output; and semi-
supervised learning uses little-supervised data with a large
amount of unlabeled data for pattern recognition analysis. [30]
Similarly, modern rideshare services like Uber and Didi
Chuxing, among others, have raised the possibilities of
enormous data collection. AI can use this data to efficiently
estimate passenger demand, avoiding empty cars and therefore
reducing congestion and energy usage [31].
● Luo et al proposes a deep learning model that takes into
account Multi-View, Spatial, and Temporal (DMVST)
Network. The authors gathered large-scale ridesharing demand
information from DiDi Chuxing in the Chinese city of
Guangzhou. They used a combination of Local CNN, which
captures local areas in relation to their surroundings, and a
long-short-term memory networks (LSTM), which models
temporal aspects.The results showed that the proposed model
outperformed others [31].
● Similarly, Xu, Ying et al used a Multi-layer Perceptron
Neural Network to forecast taxi demand in Tokyo, Japan.
They gather data from the Taxi Probe system, in which cabs
are outfitted with sensors that capture information (e.g., taxi
location) [32]. The results revealed that using 4 hours of
historical demand data with fifty neurons in the layers that
were concealed improved forecast accuracy.
III. METHODOLOGY
This section analyzes the need of AI in traffic management
and challenges that are being faced to implement it.
A. WHY IS AI NEEDED IN TRAFFIC MANAGEMENT?
After the huge increase in the population and extensive usage
of private means of transportation in the last 20 years, major
cities across the world are dealing with the issue of traffic
congestion daily, especially metropolitan cities. Following are
some key problems that arise from the aforementioned issue:
B. AIR POLLUTION
The biggest and significant problem which arises due to traffic
congestion is air pollution. As we know that many
manufacturers are introducing their lineups of electric vehicles
(EVs) but still the major percentage of vehicles across the
globe run on hydrocarbon fuels which release
toxic/greenhouse gasses into the atmosphere, worsening the
already bad situation of the atmosphere [23].
C. STRESS ON CITY INFRA-STRUCTURE
Traffic congestion puts pressure on city infra-structure in
several ways. It can increase wear and tear on roads and
highways, leading to the need for more frequent repairs and
maintenance. Additionally, traffic congestion can increase
travel times and make it difficult for emergency vehicles to
reach their destinations quickly [18]. This can have serious
implications for public safety.

23
a) REDUCTION IN PRODUCTIVITY
In addition to harming the environment, traffic congestion
has serious negative effects on the economy, particularly in
terms of lost productivity. Due to traffic congestion it takes
longer for goods to be transported and people to get to work.
b) ENERGY WASTAGE
Traffic congestion is a significant reason for energy wastage
on the planet. Increased idling of vehicles in the congestion
leads to the burning of more fuel which strains the already
scarce source of fossil fuels. Businesses may incur higher
operating costs as a result of traffic congestion. Greater fuel
usage and transportation costs result from longer delivery
times. Businesses may also need to set aside funds to handle
logistics and find alternate ways to reduce delays, which can
have a negative impact on production.
c) SOCIAL EFFECT
There are many social side effects of traffic congestion.
Many studies have shown that the dwellers of metropolitan
cities experience frustration and mental stress due to traffic
congestion. Reduced social interactions and reduced access
to opportunities also add up to the issue.
B. WHAT ARE THE CHALLENGES IN WIDESPREAD
APPLICATION OF AI BASED TRAFFIC MANAGEMENT
SYSTEMS?
After discussing the need for Artificial Intelligence based
traffic management systems & current proposed designs and
development of AI-based traffic management systems, we
need to ponder upon the fact that, why we are lacking real-
world practical use cases of AI in traffic management.
a) CAPITAL INVESTMENT
Out of the many challenges that are hindering the widespread
adoption of smart traffic management systems, the core reason
is the high cost of technology. AI systems require data to
process. To collect data related to traffic high-end CCTV
cameras and proximity sensors are required which are very
costly. In addition to that, processing a large amount of data
requires heavy-duty computer hardware which also adds ups
to the cost.
b) ABSENCE OF INFRASTRUCTURE
To implement an Artificial Intelligence based system, certain
civil infrastructure is required, for example, road intersections
are required to be made labeled and marked to help the
computer vision algorithms to identify the roads. Many third-
world metropolitan cities lack the basic infrastructure to install
such systems. Also, the road networks require to be built
according to certain standards across the city to help train
Artificial Intelligence algorithms for efficient working [22].
C) COMPLEXITY
AI-based traffic management systems can be complex, and it
can be difficult to design and implement them in a way that
works well in the real world. Although there are many systems
that researchers have proposed in recent years, most of them
are simulation models or they work in a highly simulated and
controlled environment.
D) ETHICAL & SOCIAL ISSUES
AI-based traffic management systems rely on data from
various sources such as cameras, GPS, and sensors, which
raise concerns about data collection and privacy, also there is a
lack of trust and understanding of the technology among the
general public and decision-makers, which makes it harder to
implement and maintain these systems.
E) PRIVACY CONCERNS
AI structures need rights to many data sources it may include a
personal data from different device, a surveillance cameras, a
GPS information all these increases the concerns and alarms
about their privacy and security. Matching the requirements
for information gathering with confirming one’s privacy rights
is important. It needs to implement a vigorous information
security measures and safeguarding the transparency in
information procedure to advance community trust.
IV. RESULTS
Table I describes the summary of numerous sensors and techniques/algorithm
used in mentioned papers.
TABLE I
SUMMARY OF RESOURCES USED IN THE PROPOSED SYSTEMS
DISCUSSED [17] [18]
S.NO
Paper Name
Sensors
Algorithm/Technique
1
Smart control of
traffic light using
Artificial
Intelligence
Camera
Neural Network, YOLO
2
Ai Traffic Control
System based on
Deep stream and
IOT using
NVIDIA Jetson
Camera
NVIDIA ML, SSD, IoT,
MQTT
Nano
3
Traffic Light
Control and
Violation
Detection Using
Image Processing
Camera
Image Processing,
“openCV”
4
Smart Traffic
Light Controller
Using Image
Processing
Camera
&
Sound
Sensor
Image processing
5
The role of Blockchain,
AI and IOT for smart
road traffic
management system
Speed,
GPS,
Camera,
etc
AI, Blockchain & IoT

24
FIGURE 1. DETAILS OF THE EXTRA TRAVEL TIME (IN
%) AS COMPARED TO THE AVERAGE UNCONGESTED
CONDITIONS. [6]
V. DISCUSSION & CONCLUSION
This paper highlights the problems caused by the absence of
AI-based traffic management in detail. Conventional traffic
management techniques are not keeping up with the huge
increase in the volume of traffic. This paper also presents a
brief highlight of some of the proposed AI based systems in
this regard that can be implemented. Another issue which
requires our attention is the challenges that are being faced to
implement such systems in the real world. Artificial
Intelligence (AI) has the potential to play a significant role in
traffic management by providing real-time traffic data,
predicting traffic patterns, and optimizing traffic flow. AI-
powered traffic management systems can also help reduce
congestion, improve safety, and make transportation more
efficient.
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