Phase Re-service in Reinforcement Learning Traffic Signal Control
<|reference_start|>Phase Re-service in Reinforcement Learning Traffic Signal Control: 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.<|reference_end|>
@article{zhang2024phase,
    title={Phase Re-service in Reinforcement Learning Traffic Signal Control},
    author={Zhiyao Zhang, George Gunter, Marcos Quinones-Grueiro, Yuhang Zhang,
  William Barbour, Gautam Biswas, Daniel Work},
    journal={arXiv preprint arXiv:2407.14775},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14775},
    primaryClass={eess.SY cs.SY}
}