Title | ||
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Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control. |
Abstract | ||
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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. |
Year | Venue | DocType |
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2022 | International Conference on Machine Learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Liang Zhang | 1 | 0 | 0.68 |
Qiang Wu | 2 | 304 | 40.42 |
Jun Shen | 3 | 234 | 40.40 |
Linyuan Lü | 4 | 1004 | 39.33 |
Bo Du | 5 | 1662 | 130.01 |
Jianqing Wu | 6 | 0 | 0.34 |