PressLight: Learning Max Pressure Control to Coordinate Traffic Signals in Arterial Network

By Hua Wei, Chacha Chen, Guanjie Zheng, Kan Wu, Vikash V. Gayah, Kai Xu, Zhenhui Jessie Li. Knowledge Discovery and Data Mining, 2019

485 citations recorded by the source corpus.

Traffic signal control is essential for transportation efficiency in road networks. It has been a challenging problem because of the complexity in traffic dynamics. Conventional transportation research suffers from the incompetency to adapt to dynamic traffic situations. Recent studies propose to use reinforcement learning (RL) to search for more efficient traffic signal plans. However, most existing RL-based studies design the key elements - reward and state - in a heuristic way. This results in highly sensitive performances and a long learning process. To avoid the heuristic design of RL elements, we propose to connect RL with recent studies in transportation research. Our method is inspired by the state-of-the-art method max pressure (MP) in the transportation field. The reward design of our method is well supported by the theory in MP, which can be proved to be maximizing the throughput of the traffic network, i.e., minimizing the overall network travel time. We also show that our concise state representation can fully support the optimization of the proposed reward function. Through comprehensive experiments, we demonstrate that our method outperforms both conventional transportation approaches and existing learning-based methods.

Subfields: Building and Construction, Control and Systems Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/64993b08a97d89411276a1336c7df0b279b895b9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Hua Wei
2. Guanjie Zheng
3. Nizar Bouguila
4. Saeed Rahmani
5. Asiye Baghbani
6. Zachary Patterson
7. Chacha Chen
8. Kai Xu

In Control and Systems Engineering

1. Hua Wei
2. Guanjie Zheng
3. Chacha Chen
4. Kai Xu
5. Nan Xu
6. Zhenhui Jessie Li
7. Nizar Bouguila
8. Saeed Rahmani

In Transportation

1. Yuanhao Xiong
2. Hua Wei
3. Chacha Chen
4. Nizar Bouguila
5. Yanmin Zhu
6. Saeed Rahmani
7. Nan Xu
8. Asiye Baghbani

Citations in the corpus

• Graph Neural Networks for Intelligent Transportation Systems: A Survey (2023, result): “Comparing the results of their algorithm with baselines (fixed time, Max Pressure [159], CoLight [157], and PassLight [166]), they concluded that GPlight is able to increase the throughput and reduce the delay at the studied intersections.”
• CoLight: Learning Network-level Cooperation for Traffic Signal Control (2019, methodology): “Following existing studies [28, 30], we use the average travel time to evaluate the performance of different models for traffic signal control.”
• Toward A Thousand Lights: Decentralized Deep Reinforcement Learning for Large-Scale Traffic Signal Control (2020, methodology): “• PressLight (Wei et al. 2019a): a recently developed learning-based method that incorporates pressure in the state and reward design fo the RL model.”