IMPLEMENTATION OF A COMPUTER
VISION GUIDED INTELLIGENT TRAFFIC
LIGHT CONTROL SYSTEM FOR ADAPTIVE
TRAFFIC LIGHT CONTROL AND URBAN
MOBILITY ENHANCEMENT IN BENIN CITY
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.
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.
Aim and Objective
This research project aims to design and implement an intelligent traffic light control
system using an Image processing technique, with the following 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.
Meta Analysis
Author(s)
Title of Literature
Description of work
Results
Sun, L., et al. (2023)
A reinforcement learning
approach for adaptive traffic
signal control with mixed
traffic flows
Proposed a deep reinforcement
learning (DRL) model for adaptive
traffic signal control considering
both vehicles and pedestrians.
Reduced waiting time by 15%,
travel time by 12% & improved
pedestrian safety with lower
crossing time and violation
rates.
Wang, R., et al. (2022)
Decentralised multi-agent
reinforcement learning for
adaptive traffic signal control
in a network of intersections
Developed a decentralised multiagent reinforcement learning
(MARL) system for coordinating
traffic lights across multiple
intersections.
Achieved 20% reduction in
average travel time and 18%
decrease in fuel consumption
compared to fixed timing.
Improved traffic flow
smoothness and reduced stopand-go behaviour.
Elbery, A., et al. (2021)
Real-time traffic signal
control using genetic
algorithm optimization with
vehicle-to-infrastructure
(V2I) communication
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.
Qin, Z., et al. (2019)
A deep Q-learning based
approach for adaptive traffic
signal control with
consideration of
environmental factors
Proposed a deep Q-learning
algorithm for traffic signal control
that incorporates air quality and
noise pollution metrics into the
optimization process.
Improved travel time, air
quality, and noise pollution
compared to fixed-time
control. Showed trade-offs
based on varying traffic
Methodology
• This traffic control system utilizes Raspberry Pi 4B connected to POE-powered
cameras at intersections to monitor traffic. The system processes real-time footage to
determine vehicle density using image subtraction and calculates the number of
vehicles with a specific formula. A Web API built with Microsoft Azure ML Studio,
using Linear Regression trained on a dataset of vehicle numbers and lane counts,
determines the optimal green light duration. The system operates in cycles, starting
from the north side road and moving clockwise. The Raspberry Pi, equipped with a
GSM module for internet connectivity, captures images, processes vehicle counts,
requests the API for green light duration, and controls the traffic lights accordingly,
ensuring efficient traffic flow and reduced waiting times at intersections.
Expected Results
• Dynamic Traffic Management: ITLCMS would use advanced sensors and algorithms to
adjust traffic light timings based on real-time conditions, significantly reducing
congestion and shortening travel times.
• Enhanced Safety for Pedestrians and Cyclists: Incorporates dedicated sensors and
adaptive light timings to improve safety at crossings, encouraging walking and cycling
while ensuring accessibility for those with disabilities.
• Environmental and Public Health Benefits: Implementing the ITLCMS would also lead
to reduced air pollution and noise levels due to smoother traffic flow and less idling,
contributing to a cleaner, healthier urban environment.
• Economic and Social Advantages: Improves transportation efficiency, enhancing
productivity and economic competitiveness, while reducing transportation costs and
fostering stronger, more connected communities.
Challenges and Concerns
While the expected results of the ITLCMS are promising, implementing this system poses
certain challenges:
• Data privacy concerns: Collecting and utilising real-time traffic data needs to be balanced
with the need for data privacy and security. Ensuring that the system complies with local
data protection laws and securing the data against unauthorized access are crucial.
• Infrastructure investment: Installing and maintaining the sensor network and
communication infrastructure requires significant investment and coordinated planning.
• Public awareness and acceptance: Gaining public trust and acceptance for this new
technology is crucial for its successful implementation. Changing traffic dynamics based
on AI might require time for users to adapt. Misunderstanding or mistrust of the new
system by drivers and pedestrians can lead to compliance issues.
• Environmental Factors: Benin City's weather conditions, such as heavy rain, dust, and fog,
can affect the cameras' visibility, impacting the system's accuracy in vehicle detection.
Conclusion
• This propsed ITLCMS aims to revolutionize urban mobility through real-time traffic
data analysis, using internet-connected cameras to dynamically adjust traffic light
timings. This innovative approach promises reduced congestion, improved traffic
efficiency, and enhanced safety for all road users. Distinguished by its real-time
responsiveness, scalability, and data-driven optimization, ITLCMS offers significant
advantages over traditional traffic control methods. Key challenges such as data
privacy, infrastructure requirements, and public acceptance are acknowledged. The
next phases involve detailed design, pilot testing, and stakeholder engagement,
ensuring the system's successful implementation towards creating more efficient,
safe, and sustainable urban environments.
Thank you.