Abstract: The voluminous number of vehicles present on principal roads together with ongoing road expansion projects are triggering serious roadblocks during peak hours in many places in Mauritius. Consequently, an innovative solution has been proposed using the strength of deep learning neural networks and cutting-edge computer vision methodologies to help reduce this problem. The idea is to create a reliable system that is adequate to measure traffic density and traffic flow on important roads of Mauritius in real-time. A dataset of 2800 frames was collected and used to train and test the YOLO models. A setup was designed for detecting, tracking and counting vehicles such as buses, cars, motorbikes, trucks and vans. Relevant traffic information from videos can also be retrieved to generate statistics for traffic density. Moreover, the system can estimate individual speed of vehicles as well as determining traffic flow on bidirectional roads. The overall mean counting accuracy was 96.1% and the overall mean classification accuracy was 94.4%. For traffic flow, the overall mean accuracy was 93.9%, while traffic density was estimated with an overall mean accuracy of 95.3%. In comparison with manual approaches used in Mauritius to understand the state of traffic, the proposed system is a modern, low-cost and effective solution that can adopted to potentially reduce traffic congestions and traffic accidents.

Sameerchand Pudaruth, Irfaan Mohammad Boodhun and Choo Wou Onn, “Reducing Traffic Congestion Using Real-Time Traffic Monitoring with YOLOv8” International Journal of Advanced Computer Science and Applications(IJACSA), 15(10), 2024. http://dx.doi.org/10.14569/IJACSA.2024.01510109

@article{Pudaruth2024,
title = {Reducing Traffic Congestion Using Real-Time Traffic Monitoring with YOLOv8},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01510109},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01510109},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {10},
author = {Sameerchand Pudaruth and Irfaan Mohammad Boodhun and Choo Wou Onn}
}