Machine learning solutions for adaptive traffic signal control: A review of image-based approaches
Information Technology, Birla Vishvakarma Mahavidyalaya, Mota Bazaar, Vallabh Vidyanagar, Anand, Gujarat 388120, India.
Review
World Journal of Advanced Engineering Technology and Sciences, 2024, 13(01), 476–481.
Article DOI: 10.30574/wjaets.2024.13.1.0437
Publication history:
Received on 05 August 2024; revised on 18 September 2024; accepted on 20 September 2024
Abstract:
This paper gives an overview and performance evaluation of various machine learning models implemented in management of urban traffic congestions, more specifically, adaptive traffic signal control systems. It considers a review of deep learning algorithms including R- CNN, Fast R-CNN, Faster R-CNN, SSD, YOLO v4, and YOLOv8, with regard to their efficiencies for vehicle detection and traffic prediction under varying scenarios. Certain traffic conditions, camera placements, and environmental factors—related performance for each of the models are discussed. The major performance in most of the scenarios was depicted by the YOLO v4. However, at the same time, YOLOv8 has shown potential to do much better than YOLO v4 on image processing and the resultant accuracy. It also proposes a new algorithm for traffic light timing, whose efficacy is tested using the SUMO simulation platform. While results have shown improvements in urban traffic management, a review underlines that such is in deep need of extensive real-world testing. Future directions should include views from varied angles and weather conditions, and the detection of emergency vehicles, probably with specialized datasets.
Keywords:
Urban Traffic Management; Machine Learning Models; Adaptive Traffic Signal Control; Deep Learning Algorithms
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0