Phase Re-service in Reinforcement Learning Traffic Signal Control

TL;DR

This paper introduces a reinforcement learning-based traffic signal control method that incorporates phase re-service guided by shock wave theory, significantly reducing vehicle delays and stops.

Contribution

It presents a novel integration of phase re-service with RL for traffic signals, formulated as an SMDP and optimized with PPO, improving traffic flow efficiency.

Findings

Vehicle delays reduced by up to 29.95%

Stops decreased by 26.05% on average

Significant improvements in traffic flow efficiency

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.