Phase Re-service in Reinforcement Learning Traffic Signal Control
Abstract
This article proposes a novel approach to traffic signal control that combines phase re-service with reinforcement learning (RL). The RL agent directly determines the duration of the next phase in a pre-defined sequence. Before the RL agent's decision is executed, we use the shock wave theory to estimate queue expansion at the designated movement allowed for re-service and decide if phase re-service is necessary. If necessary, a temporary phase re-service is inserted before the next regular phase. We formulate the RL problem as a semi-Markov decision process (SMDP) and solve it with proximal policy optimization (PPO). We conducted a series of experiments that showed significant improvements thanks to the introduction of phase re-service. Vehicle delays are reduced by up to 29.95% of the average and up to 59.21% of the standard deviation. The number of stops is reduced by 26.05% on average with 45.77% less standard deviation.
Cite
@article{arxiv.2407.14775,
title = {Phase Re-service in Reinforcement Learning Traffic Signal Control},
author = {Zhiyao Zhang and George Gunter and Marcos Quinones-Grueiro and Yuhang Zhang and William Barbour and Gautam Biswas and Daniel Work},
journal= {arXiv preprint arXiv:2407.14775},
year = {2024}
}
Comments
Accepted to IEEE ITSC 2024