Machine learning solutions for adaptive traffic signal control: A review of image-based approaches

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.