Abstract
With the exponential growth in motor vehicle numbers, urban traffic congestion has become a pressing issue. Traffic signal control plays a pivotal role in alleviating the problem. In modeling multi-intersection, most studies focus on communication with regional intersections. They rarely consider the cross-regional. To address the above limitation, we construct an interactive attention-based graph transformer network for traffic signal control (GTLight). Specifically, the model considers correlations between cross-regional intersections using an interactive attention mechanism. In addition, the model designs a phase-timing optimization algorithm to solve the problem of overestimation of Q-value in signal timing strategies. We validate the effectiveness of GTLight on different traffic datasets. Compared to the recent graph-based reinforcement learning method, the average travel time is improved by 28.16%, 26.56%, 25.79%, 26.46%, and 19.59%, respectively.
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Acknowledgements
This work was supported by National Natural Science Foundation of China (No. 62176088), the Key Science and Technology Research Project of Henan Province of China (Grant No. 22102210067, 222102210022), and the Program for Science and Technology Development of Henan Province (No. 212102210412 and 202102310198).
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Lv, Y., Ning, N., Li, H., Wang, L., Zhang, Y., Zhou, Y. (2024). Interactive Attention-Based Graph Transformer for Multi-intersection Traffic Signal Control. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14448. Springer, Singapore. https://doi.org/10.1007/978-981-99-8082-6_5
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DOI: https://doi.org/10.1007/978-981-99-8082-6_5
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