A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning

The optimization of traffic signal control (TSC) is critical for an efficient transportation system. In recent years, reinforcement learning (RL) techniques have emerged as a popular approach for TSC and show promising results for highly adaptive control. However, existing RL-based methods suffer fr...

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Veröffentlicht in:	Data science for Transportation Jg. 7; H. 3; S. 25
Hauptverfasser:	Li, Jianxiong, Lin, Shichao, Shi, Tianyu, Tian, Chujie, Mei, Yu, Song, Jian, Zhan, Xianyuan, Li, Ruimin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Singapore Springer Nature Singapore 01.12.2025 Springer Nature B.V
Schlagworte:	Adaptive control Computational Intelligence Data Mining and Knowledge Discovery Datasets Engineering Flow theory Machine learning Neural networks Optimization Regularization methods Simulation Simulators Traffic control Traffic flow Traffic information Traffic intersections Traffic signals Transportation systems Transportation Technology and Traffic Engineering Real-world traffic data Shockwave theory Offline reinforcement learning Traffic signal control
ISSN:	2948-135X, 2948-1368
Online-Zugang:	Volltext
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