Abstract
Intelligent Traffic Management is a crucial issue closely related to daily life and productivity, with traffic congestion being a complex and challenging problem faced by most cities. Traffic Signal Control (TSC) stands out as the most direct and effective method to tackle congestion. It aims to minimize travel time, enhance throughput, improve traffic safety, reduce emissions, and conserve energy by coordinating the direction and timing of vehicle movements at intersections. Traditional TSC methods mostly rely on simple rules, limited data, and expert knowledge, making them inadequate for increasingly complex traffic scenarios. In the context of TSC, an increasing number of researchers are turning to Deep Learning (DL) methods to address identification, decision-making, and optimization challenges. Although many reviews have examined the TSC problems and the application of Reinforcement Learning in this field, there remains a notable gap in comprehensive analyses of TSC utilizing a wider range of DL techniques, including Deep Reinforcement Learning, Federated Learning, and Meta-learning. This paper, building upon the basic concepts and traditional approaches of TSC, provides a detailed overview of the latest research advancements employing different DL methods for this issue. Experimental settings and evaluations are also introduced. Furthermore, to spark new interest in this research field, future works are proposed.
This paper was recommended by Regional Editor Takuro Sato.
