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Adaptive Congestion Control Protocol based on Meta-Reinforcement Learning for Data Communication Networks

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A Deep Learning-based Model for Traffic Signal Control using the YOLO Algorithm

by Oluwole Ayodeji Ayegbusi, Ayodeji Olusegun Akinwumi, OluwatosinLara Ogbeide, Alaba Olu Akingbesote, Joy Rotimi Obafemi, Olatunde David Akinrolabu, Odunayo Rotiba
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 63
Year of Publication: 2025
Authors: Oluwole Ayodeji Ayegbusi, Ayodeji Olusegun Akinwumi, OluwatosinLara Ogbeide, Alaba Olu Akingbesote, Joy Rotimi Obafemi, Olatunde David Akinrolabu, Odunayo Rotiba
10.5120/ijca2025924452

Oluwole Ayodeji Ayegbusi, Ayodeji Olusegun Akinwumi, OluwatosinLara Ogbeide, Alaba Olu Akingbesote, Joy Rotimi Obafemi, Olatunde David Akinrolabu, Odunayo Rotiba . A Deep Learning-based Model for Traffic Signal Control using the YOLO Algorithm. International Journal of Computer Applications. 186, 63 ( Jan 2025), 43-54. DOI=10.5120/ijca2025924452

@article{ 10.5120/ijca2025924452,
author = { Oluwole Ayodeji Ayegbusi, Ayodeji Olusegun Akinwumi, OluwatosinLara Ogbeide, Alaba Olu Akingbesote, Joy Rotimi Obafemi, Olatunde David Akinrolabu, Odunayo Rotiba },
title = { A Deep Learning-based Model for Traffic Signal Control using the YOLO Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2025 },
volume = { 186 },
number = { 63 },
month = { Jan },
year = { 2025 },
issn = { 0975-8887 },
pages = { 43-54 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number63/a-deep-learning-based-model-for-traffic-signal-control-using-the-yolo-algorithm/ },
doi = { 10.5120/ijca2025924452 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-01-31T17:28:30+05:30
%A Oluwole Ayodeji Ayegbusi
%A Ayodeji Olusegun Akinwumi
%A OluwatosinLara Ogbeide
%A Alaba Olu Akingbesote
%A Joy Rotimi Obafemi
%A Olatunde David Akinrolabu
%A Odunayo Rotiba
%T A Deep Learning-based Model for Traffic Signal Control using the YOLO Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 63
%P 43-54
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With increasing urbanization and population growth, traffic congestion has become a significant challenge in cities, resulting in delays, excessive fuel consumption, pollution, and stress-related health issues. This problem stems from the imbalance between transportation demand and road infrastructure supply. Conventional methods, such as using the manual or fixed-time control systems, have proven inadequate as they fail to adapt to real-time traffic conditions. This research therefore introduces a deep learning-based smart traffic signal control model utilizing the YOLO (You Only Look Once) algorithm to mitigate traffic congestion in real time. The model captures live images from traffic junction cameras, detects vehicles, calculates traffic density, and dynamically adjusts signal timers, prioritizing lanes with higher traffic. The system’s architecture includes five phases: data acquisition, data pre-processing, training, signal control, and smart traffic control. The Open Images Dataset was utilized for the prototype demonstration, where the dataset was labeled and converted into a YOLO compatible format during the preprocessing phase to prepare it for training. The model was implemented using Python programming language. Evaluation revealed a 27% increase in traffic flow and a 50% reduction in vehicle waiting time. The research concluded that the YOLO based traffic control system provides a more effective solution to urban traffic congestion and is recommended for integration with CCTV cameras to facilitate efficient traffic management in cities.

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Index Terms

Computer Science
Information Sciences
Deep Learning
Traffic Management
Object Detection
Artificial Intelligence
Pattern Recognition
Computer Vision

Keywords

YOLO Algorithm Traffic Signal Control Real-Time Traffic Management Deep Learning Smart Traffic Systems Vehicle Detection

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