Learning Transformer-based Cooperation for Networked Traffic Signal Control

Chen Zhao, Xingyuan Dai, Xiao Wang, Lingxi Li, Yisheng Lv, Fei-Yue Wang

Abstract: Networked traffic signal control (NTSC) is essential for intelligent transportation systems. How to control multiple intersections in a cooperative way based on traffic conditions is critical for the success of NTSC. This paper proposes a Transformer-based cooperation mechanism (TCM) with the consideration of dynamic modeling and scale requirements simultaneously for large-scale traffic network control. Considering the physical constraints in traffic scenarios, a relative position encoding is designed to embed into TCM to characterize traffic conditions better. With the shared TCM module, intersection controllers could adequately exploit spatial-temporal correlations and adaptively capture global traffic dynamics, guiding them to explore collaborative traffic strategies more efficiently. Experimental results on two real-world datasets demonstrate that the suggested strategy greatly outperforms the state-of-the-art methods.