FINAL YEAR PROJECT PROPOSAL
ON
COMPUTER VISION GUIDED INTELLIGENT TRAFFIC LIGHT
CONTROL SYSTEM FOR ADAPTIVE TRAFFIC LIGHT CONTROL
AND URBAN MOBILITY ENHANCEMENT IN BENIN CITY
BY
AGELOISA OHIOSUMUAN KENNEDY
ENG1804709
OMATSULI TORITSEJU ANOINTED
ENG1804783
ENEKAMMA EMMANUEL CHUKWUNONSO
ENG1804732
OGHENEKOME JEFFREY NAYERI
ENG1703958
OZONO OSHIOKE MALCOLM
ENG1804800
SUPERVISED BY
ENGR. A. OBAYUWANA
JANUARY 2024
ABSTRACT
This project aims to develop and implement an intelligent traffic light control
system that can address the complexities of urban mobility, with the aim of
enhancing efficiency and reducing congestion. The proposed system will
optimise signal timings based on real-time traffic data and adapt to changing
traffic conditions, thereby improving the overall flow of vehicles and
minimising delays. The project seeks to integrate advanced sensor technologies,
machine learning algorithms, and communication networks to achieve this
objective. Specifically, sensors will be placed at intersections to collect traffic
data, machine learning models will analyse this data to predict traffic patterns,
and communication networks will facilitate coordination between intersections.
The expected outcome of this project is a sophisticated traffic light control
system that significantly enhances urban mobility, reduces travel times, fuel
consumption, and environmental impact. The project aims to lay the foundation
for smarter, adaptive traffic management systems, contributing to the
advancement of intelligent urban mobility solutions.
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CHAPTER ONE
INTRODUCTION
1.1 Background of study
A smart traffic light system uses sensors at various locations to adjust traffic lights
depending on real-time road conditions. The system can adapt and adjust to the
current situations on the road, curbing inefficient problems such as vehicles being
delayed or traffic jams. Implementing an intelligent traffic light control system
could be a possible solution to significant travel time wastage caused by the large
number of vehicles on the roads in Benin City. The system can analyse traffic
density using computer vision and image processing techniques, such as car
counting, and respond to growing traffic demand, forming part of an integrated
approach to traffic management.
1.2 Problem Statement
Benin City faces a challenge in managing traffic and ensuring safety at
intersections. A smart traffic light system will be developed to optimise traffic
flow, enhance safety, and provide insights for traffic management personnel. The
system will use advanced technologies, such as computer vision and cloud-based
communication, to monitor traffic in real-time and adjust signal timings
accordingly
1.3 Aim and Objective
This research aims To design and implement an intelligent traffic light control
system using an Image processing technique.
OBJECTIVES
● Measurement of traffic density.
● To Evaluate the effectiveness of the smart Traffic light system in
dynamically adjusting signal timings to optimise traffic flow and reduce
congestion.
● Implementation of a cloud-based storage system for efficient and secure
storage of real-time traffic data, enabling easy retrieval and analysis.
● Exploration of the system's potential contributions to sustainable urban
development by reducing traffic-related emissions and promoting
efficient resource allocation.
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By achieving these aims and objectives, an Intelligent Traffic Light Control
Model System can transform urban mobility, creating a more efficient, safe,
sustainable, and user-friendly transportation network for all.
1.4 Scope of the study
The scope of this study encompasses the development, implementation, and
evaluation of a smart traffic light system designed to address specific challenges in
urban transportation.
1.5 Relevance of Study
The implementation of a smart traffic light system has immense significance in
modern urban infrastructure and transportation management. Conventional traffic
control systems often fail to adapt dynamically to complex traffic patterns, leading
to congestion, increased travel times, and safety risks. The proposed smart traffic
light system integrates cutting-edge technologies such as computer vision and
cloud-based communication to create a more responsive and adaptive traffic
management infrastructure.
The smart traffic light system is built to optimise traffic flow by dynamically
adjusting signal timings based on real-time traffic conditions. This adaptability
helps reduce congestion, improve travel efficiency, and create a more seamless
urban transportation experience.
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CHAPTER TWO
LITERATURE REVIEW
Meta-Analysis Table
Author(s)
Sun, L., et al.
(2023)
Wang, R., et al.
(2022)
Title of
Literature
Description of
Work
A
reinforcement
learning
approach for
adaptive traffic
signal control
with mixed
traffic flows
Reduced waiting
Proposed a deep time by 15%,
reinforcement
travel time by
learning (DRL) 12% &
model for
improved
adaptive traffic
pedestrian safety
signal control
with lower
considering both crossing time
vehicles and
and violation
pedestrians.
rates.
Requires high
computational
power &
real-time traffic
data collection.
Limited testing
environment
(simulation)
Decentralised
multi-agent
reinforcement
learning for
adaptive traffic
signal control
in a network of
intersections
Achieved 20%
reduction in
average travel
time and 18%
decrease in fuel
consumption
compared to
fixed timing.
Improved traffic
flow smoothness
and reduced
stop-and-go
behaviour.
Requires
communication
channels
between
intersections and
assumes
availability of
accurate traffic
flow data. May
not be applicable
to complex
network
topologies.
Developed a
decentralised
multi-agent
reinforcement
learning
(MARL) system
for coordinating
traffic lights
across multiple
intersections.
Results
Limitations
4
Utilised V2I
communication
to gather
real-time traffic
data and a
genetic
algorithm to
optimise traffic
signal timing
dynamically.
Improved travel
speed by 10%
and reduced
queue length by
15%. Effective
in handling
unexpected
traffic events.
Relies heavily
on V2I
infrastructure
and data
accuracy.
Optimization
algorithms can
be
computationally
expensive for
large
intersections.
Li, W., et al.
(2020)
An online
adaptive fuzzy
logic approach
for intelligent
traffic signal
control
Built an online
adaptive fuzzy
logic controller
for traffic signal
timing based on
real-time traffic
volume and
queue length.
Reduced travel
time by 12% and
waiting time by
14% compared
to fixed-time
control, while
maintaining
stable traffic
flow under
fluctuating
demand.
Accurate and
continuous
traffic data
collection is
essential. Fuzzy
logic rules
require specific
intersection
tuning.
Qin, Z., et al.
(2019)
Proposed a deep
A deep
Q-learning
Q-learning
algorithm for
based approach traffic signal
for adaptive
control that
traffic signal
incorporates air
control with
quality and noise
consideration
pollution metrics
of
into the
environmental optimization
factors
process.
Improved travel
time, air quality,
and noise
pollution
compared to
fixed-time
control. Showed
trade-offs based
on varying
traffic
conditions.
The Q-learning
model needs
large datasets
and may not suit
existing traffic
management
systems due to
complex
optimization.
Elbery, A., et al.
(2021)
Real-time
traffic signal
control using
genetic
algorithm
optimization
with
vehicle-to-infra
structure (V2I)
communication
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CHAPTER THREE
EXPECTED METHOD FOR INTELLIGENT TRAFFIC LIGHT CONTROL
SYSTEM
Our Intelligent Traffic Light Control and Management System (ITLCMS) will
capture real-time traffic footage from multiple cameras, extracts relevant features,
and sends preprocessed data to the Azure ML Studio Web API for analysis. The
trained machine learning model would predict future traffic patterns and optimise
traffic light timing for efficient traffic flow. The system is monitored for
performance, periodically retrained, and optimised to enhance its effectiveness.
Ethical considerations such as data privacy, explainability, transparency, and social
equity would also be ensured.
A. System Overview
The traffic control system would use cameras installed at each intersection,
which would be connected to an internet based cloud server to determine when the
traffic light should turn green. The green light duration depends on the number of
vehicles waiting at the intersection, the number of lanes on the road, and the
number of vehicles present on the road. If there are more vehicles waiting, the
green light will remain on for a longer period to allow traffic to pass through. If
there are fewer lanes, the green light time will be longer, and vice versa. The green
light at each intersection follows a cycle starting from the north side road,
followed by the east side road in a clockwise direction. This system helps to
reduce the waiting time for drivers at intersections. In summary, the traffic control
system would use cameras to determine when the traffic light should turn green,
taking into account the number of vehicles waiting at the intersection road, the
number of lanes on the road, and the number of vehicles present on the road. The
green light duration follows a cycle at each intersection, starting from the north
side road, followed by the east side road in a clockwise direction. This system
should help to reduce the waiting time for drivers at intersections and ensure
efficient traffic flow.
B. Hardware and Connections
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A Raspberry Pi 4B would be connected to four cameras via USB. These
cameras are powered by POE and are compatible with Gphoto2. Additionally, the
Raspberry Pi 4B is connected to a GSM module, allowing it to connect to the
internet and access web APIs. The device is powered by a 5V DC power supply.
We are using the Microsoft Azure ML Studio Web API service to access the
online ML model from the Raspberry Pi 4B.
The Raspberry Pi is a low-cost, credit-card-sized computer developed in the UK
that allows users of all ages to explore computing and learn programming
languages such as Scratch and Python. It can be plugged into a computer monitor
or TV and used with a standard keyboard and mouse. The Raspberry Pi project
was initially aimed at promoting basic computer science education in schools and
developing countries.
POE-powered traffic cameras are an innovative use of traffic surveillance
technology, placed along busy roads and intersections of the highway. These
cameras are Gphoto2 compatible, which supports not just retrieving images from
camera devices but also allows upload, remote-controlled configuration, and
capture.
A GSM modem or module uses mobile telephone technology to establish a data
connection with a remote network. They require a SIM to identify themselves to
the network. On the other hand, a Raspberry Pi HAT is an expansion board
designed to work with a specific version of a Pi. By using AT commands, a GSM
module can provide 4G/5G internet connectivity to the RPI4 B. HATs usually
perform more complex tasks than modules and other smaller expansion boards.
C. Vehicle Detection
The traffic management system uses video cameras installed at a junction
to capture footage of all four sides of the road. This footage is then processed
using image processing techniques to determine the vehicle density of each
road. An algorithm then controls the traffic lights based on this information.
Cameras are installed on each road to calculate the number of vehicles using
image processing. A reference image of an empty lane is used to accurately
count the number of vehicles. Real-time images are captured every second, and
the system ensures smooth traffic flow by controlling traffic lights based on the
number of cars present.
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We then subtract the real-time image from the reference image to filter
out the background and obtain the vehicular density of the road. Finally, we
calculate a constant value to determine the number of vehicles on the road. The
formula used for calculation is as follows:
x = Height of camera from road * Number of rows in subtracted image
matrix * Number of columns in subtracted image * Number of frames per
second in video
number = number / x
This provides us with the approximate number of vehicles on the road,
considering that a larger vehicle will cover relatively more area and more time
to pass the traffic junction. This process is repeated every second for all sides of
the junction.
D. Web API
This is a project that uses a Raspberry Pi to run a Python script which
calls a Web API built with Microsoft Azure ML Studio. The Web API model is
structured using Linear Regression and trained using a dataset with three
columns: Number of Vehicles, Number of Lanes, and the time it takes for traffic
to pass through an intersection. Linear Regression is a supervised machine
learning algorithm that predicts continuous output values with a constant slope,
such as sales and prices. In contrast, classification algorithms classify data into
distinct categories..
We have utilised 80% of the total dataset as training data, and the
remaining 20% has been used as testing data. We have called the web API after
detecting the number of vehicles on the road. To request the web API, we send
the current number of vehicles and the number of lanes on the road using the
HTTP POST method. The web API returns the duration for which the traffic
signal needs to be green in seconds. We have used 80% of the total data set as
training data and the remaining 20% as testing data. The web API is called after
the number of vehicles has been detected. The web API is requested by sending
the current number of vehicles on the road and the number of lanes on that road
with the HTTP POST method. The web API returns the time in seconds for
which the traffic signal needs to be green.
E. Raspberry PI and Software: The Raspberry Pi 4B serves as a central
hub for various operations that include image capture, image processing for
vehicle count, requesting web API, and controlling traffic lights. The Pi is
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connected to the internet via a GSM module HAT and the cameras are
connected via USB connections. After a traffic light cycle, the camera on the
first lane captures an image and calculates the number of vehicles using Python
libraries. An API request is made with two parameters, and the API responds
with the optimal time required to clear traffic when the light is green. The
Python script controls the traffic light and turns the first lane green for the time
specified in the API response. The same process repeats for the second lane
indefinitely.
WORKFLOW PROCESS
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CHAPTER FOUR
EXPECTED RESULTS
The Intelligent Traffic Light Control Model System (ITLCMS) is a cutting-edge
approach to urban mobility that uses data-driven and adaptive techniques for traffic
management. With the help of a network of sensors and advanced algorithms, the
ITLCMS can adjust traffic light timings based on real-time traffic conditions,
leading to reduced congestion, shorter travel times, improved safety for pedestrians
and cyclists, and significant environmental benefits such as reduced air pollution,
noise reduction, and energy conservation. The system can prioritise pedestrian
safety by adapting the timing of traffic lights to ensure safer and more efficient
crossings, while also promoting healthier lifestyles and reducing traffic congestion.
The ITLCMS offers several benefits to urban communities, including increased
productivity, enhanced economic competitiveness, and reduced transportation
costs. Moreover, the system fosters a healthier and more livable urban environment
by reducing air and noise pollution, encouraging social interaction, and promoting
outdoor recreation activities.
However, implementing the ITLCMS poses certain challenges, such as
balancing the need for real-time traffic data with data privacy and security
concerns, and gaining public trust and acceptance for this new technology. By
addressing these challenges and fostering public engagement, the ITLCMS has the
power to transform urban transportation and establish smarter, greener, and more
livable cities.
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CHAPTER FIVE
CONCLUSION: A VISION FOR SMARTER TRAFFIC AND SMOOTHER
CITIES
This proposal outlines an Intelligent Traffic Light Control System
(ITLCMS) that can revolutionise urban mobility by adjusting traffic light timing
based on real-time traffic conditions. The system can reduce congestion, waiting
times, improve efficiency, resource utilisation, safety, and accessibility.
Compared to conventional systems, the ITLCMS offers real-time
responsiveness, scalability, adaptability, and data-driven optimization.
Implementing the system requires addressing data privacy, communication
infrastructure, and public awareness concerns. Next steps include system
design, pilot testing and evaluation, and public engagement. The ITLCMS can
pave the way for smarter, more efficient, and sustainable cities.
REFERENCES
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TRAFFIC LIGHT CONTROL USING IMAGE PROCESSING.
2.M,C.,C,S.,B,C.,P,P.K.,&C,S.(2013).TRAFFIC CONTROL USING DIGITAL
IMAGE PROCESSING.Department of Electronics and Communications
Engineering.Greenfields: ISSN (Print): 22788948 volume 2.
3. Li, Y., Zhang, Q., Chen, W., & Liu, Y. (2018). "Traffic Flow Analysis Using
Image Processing Techniques." IEEE Transactions on Intelligent Transportation
Systems, 19(12), 3930-3945.
4. Zhang, H., Wang, J., Li, X., & Zhou, L. (2019). "Real-Time Vehicle
Detection and Tracking in Urban Traffic." Proceedings of the IEEE
International Conference on Computer Vision.
5. Chen, Y., Wu, X., & Zhang, S. (2017). "Object Recognition for Intelligent
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