A Novel Deep Reinforcement Learning Approach to Traffic Signal Control with Connected Vehicles

• Published in: Applied sciences Vol. 13; no. 4; p. 2750
• Main Authors: Shi, Yang, Wang, Zhenbo, LaClair, Tim J., Wang, Chieh (Ross), Shao, Yunli, Yuan, Jinghui
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-02-2023; MDPI
• Subjects: Analysis; Artificial intelligence; autoencoder neural network; Control algorithms; Control methods; Deep learning; deep reinforcement learning; Dynamic programming; Energy consumption; Energy efficiency; ENGINEERING; Genetic algorithms; Infrastructure; Methods; Optimization techniques; Reinforcement; representation learning; Simulation; Stochasticity; Traffic accidents & safety; Traffic congestion; Traffic control; Traffic flow; Traffic models; traffic signal control; Traffic signals; Traffic speed; Traffic volume; Transportation systems; Vehicles; Germany
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13042750

The advent of connected vehicle (CV) technology offers new possibilities for a revolution in future transportation systems. With the availability of real-time traffic data from CVs, it is possible to more effectively optimize traffic signals to reduce congestion, increase fuel efficiency, and enhance road safety. The success of CV-based signal control depends on an accurate and computationally efficient model that accounts for the stochastic and nonlinear nature of the traffic flow. Without the necessity of prior knowledge of the traffic system’s model architecture, reinforcement learning (RL) is a promising tool to acquire the control policy through observing the transition of the traffic states. In this paper, we propose a novel data-driven traffic signal control method that leverages the latest in deep learning and reinforcement learning techniques. By incorporating a compressed representation of the traffic states, the proposed method overcomes the limitations of the existing methods in defining the action space to include more practical and flexible signal phases. The simulation results demonstrate the convergence and robust performance of the proposed method against several existing benchmark methods in terms of average vehicle speeds, queue length, wait time, and traffic density.