Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2021

Abstract

Traffic signal control has achieved significant success with the development of reinforcement learning. However, existing works mainly focus on intersections with normal lanes with fixed outgoing directions. It is noticed that some intersections actually implement dynamic lanes, in addition to normal lanes, to adjust the outgoing directions dynamically. Existing methods fail to coordinate the control of traffic signal and that of dynamic lanes effectively. In addition, they lack proper structures and learning algorithms to make full use of traffic flow prediction, which is essential to set the proper directions for dynamic lanes. Motivated by the ineffectiveness of existing approaches when controlling the traffic signal and dynamic lanes simultaneously, we propose a new method, namely MT-GAD, in this paper. It uses a group attention structure to reduce the number of required parameters and to achieve a better generalizability, and uses multitimescale model training to learn proper strategy that could best control both the traffic signal and the dynamic lanes. The experiments on real datasets demonstrate that MT-GAD outperforms existing approaches significantly.

Keywords

Multidisciplinary Topics and Applications: Transportation, Machine Learning Applications: Applications of Reinforcement Learning

Discipline

Artificial Intelligence and Robotics | Numerical Analysis and Scientific Computing | Transportation

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21), Virtual Conference, 2021 August 19-26

First Page

3642

Last Page

3648

Identifier

10.24963/ijcai.2021/501

Publisher

IJCAI

City or Country

Montreal, Canada

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.24963/ijcai.2021/501

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