A Survey on Traffic Signal Control Methods

TL;DR

This survey reviews recent advances in traffic signal control, emphasizing the shift from rule-based systems to machine learning approaches like reinforcement learning, highlighting opportunities for interdisciplinary research.

Contribution

It provides a comprehensive overview of traditional and modern traffic signal control methods, focusing on the integration of reinforcement learning techniques.

Findings

Reinforcement learning is increasingly used for traffic signal control.

Modern methods leverage richer data and computing power.

Traditional rule-based systems are being replaced by intelligent algorithms.

Abstract

Traffic signal control is an important and challenging real-world problem, which aims to minimize the travel time of vehicles by coordinating their movements at the road intersections. Current traffic signal control systems in use still rely heavily on oversimplified information and rule-based methods, although we now have richer data, more computing power and advanced methods to drive the development of intelligent transportation. With the growing interest in intelligent transportation using machine learning methods like reinforcement learning, this survey covers the widely acknowledged transportation approaches and a comprehensive list of recent literature on reinforcement for traffic signal control. We hope this survey can foster interdisciplinary research on this important topic.