CoLight: Learning Network-level Cooperation for Traffic Signal Control

By Hua Wei, Nan Xu, Huichu Zhang, Guanjie Zheng, Xinshi Zang, Chacha Chen, Weinan Zhang, Yanmin Zhu, Kai Xu, Zhenhui Jessie Li. International Conference on Information and Knowledge Management, 2019

527 citations recorded by the source corpus.

Cooperation among the traffic signals enables vehicles to move through intersections more quickly. Conventional transportation approaches implement cooperation by pre-calculating the offsets between two intersections. Such pre-calculated offsets are not suitable for dynamic traffic environments. To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication. Specifically, for a target intersection in a network, CoLight can not only incorporate the temporal and spatial influences of neighboring intersections to the target intersection, but also build up index-free modeling of neighboring intersections. To the best of our knowledge, we are the first to use graph attentional networks in the setting of reinforcement learning for traffic signal control and to conduct experiments on the large-scale road network with hundreds of traffic signals. In experiments, we demonstrate that by learning the communication, the proposed model can achieve superior performance against the state-of-the-art methods.

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

View Paper: https://www.semanticscholar.org/paper/40968cf84b03ba9643278cb6c7c878e4accdb8cb

How researchers see this paper

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

In Automotive Engineering

1. Guanjie Zheng
2. Hua Wei
3. Chacha Chen
4. Kai Xu
5. Zhenhui Jessie Li
6. Eugene Vinitsky
7. Alexandre M. Bayen
8. Nizar Bouguila

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. Vikash V. Gayah
7. Zhenhui Jessie Li
8. Nizar Bouguila

Citations in the corpus

• Flow: A Modular Learning Framework for Mixed Autonomy Traffic (2017, methodology): “Deep RL has been used for traffic prediction [85], [86] and control [87], [88], [89].”
• 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.”
• A Survey on Traffic Signal Control Methods (2019, methodology): “• Phase: The phase information is usually integrated into the state through an index of current phase in the pre-defined signal phase groups[El-Tantawy et al. 2013; van der Pol et al. 2016; Wei et al. 2018].”
• Graph Neural Networks for Intelligent Transportation Systems: A Survey (2023, methodology): “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.”