Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control

Liang Zhang; Qiang Wu; Jun Shen; Linyuan Lü; Bo Du; Jianqing Wu

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arXiv · 2112.10107

Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control

Liang Zhang·Qiang Wu·Jun Shen·Linyuan Lü·Bo Du·Jianqing Wu

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Abstract

Many studies confirmed that a proper traffic state representation is more important than complex algorithms for the classical traffic signal control (TSC) problem. In this paper, we (1) present a novel, flexible and efficient method, namely advanced max pressure (Advanced-MP), taking both running and queuing vehicles into consideration to decide whether to change current signal phase; (2) inventively design the traffic movement representation with the efficient pressure and effective running vehicles from Advanced-MP, namely advanced traffic state (ATS); and (3) develop a reinforcement learning (RL) based algorithm template, called Advanced-XLight, by combining ATS with the latest RL approaches, and generate two RL algorithms, namely "Advanced-MPLight" and "Advanced-CoLight" from Advanced-XLight. Comprehensive experiments on multiple real-world datasets show that: (1) the Advanced-MP outperforms baseline methods, and it is also efficient and reliable for deployment; and (2) Advanced-MPLight and Advanced-CoLight can achieve the state-of-the-art.

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BibTeX RIS

Liang Zhang, Qiang Wu, Jun Shen, Linyuan Lü, Bo Du, Jianqing Wu. 2021-12-19. Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control. https://arxiv.org/abs/2112.10107

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