Ji, Qingyuan (Zhejiang Lab), Zhu, Yongdong (Zhejiang Lab), Qu, Xin (Zhejiang Lab), Jin, Junchen (KTH Royal Institute of Technology)

Trans-Sig: Knowledge Transfer for Traffic Signal Control from Spatial-Temporal Perspective

Scheduled for presentation during the Regular Session "Traffic signal control" (WeAT9), Wednesday, September 25, 2024, 10:50−11:10, Salon 17

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

In urban environments, traffic flow can display a wide range of flow patterns and turning ratios, necessitating the strategic allocation of spatio-temporal resources to improve intersection efficiency. While a significant body of research has been dedicated to signal timing optimizations via model-based or learning-based methodologies, there is a noticeable lack of studies that investigate the modification of lane configurations to maximize space resources in this setting. Moreover, model-based algorithms can be computationally demanding and difficult to train in the absence of ample data. To tackle these issues, we introduce Trans-Sig, a transfer learning recommendation framework designed to aid decision-support in intersection traffic control. Trans-Sig capitalizes on dynamic lanes, a recent innovation aimed at promoting more efficient utilization of space resources. Adopting a Hierarchical Reinforcement Learning (HRL) strategy, Trans-Sig employs two reinforcement learning agents to address this co-optimization challenge. To enable the application of traffic control knowledge from homogeneous intersections under similar traffic flows, Trans-Sig integrates pre-trained agent pools and offers a pipeline for knowledge transfer from pre-trained to target intersections via model and sample supervision mechanisms. The effectiveness and superiority of Trans-Sig are validated through simulated intersections under various flows, where it outperforms models that optimize space or time resources independently.