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Paper WeAT9.2

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

Keywords Road Traffic Control, Data Mining and Data Analysis, Theory and Models for Optimization and Control

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.

 

 

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