March 14, 2024, 4:42 a.m. | Maonan Wang, Aoyu Pang, Yuheng Kan, Man-On Pun, Chung Shue Chen, Bo Huang

cs.LG updates on

arXiv:2403.08337v1 Announce Type: cross
Abstract: Traffic congestion in metropolitan areas presents a formidable challenge with far-reaching economic, environmental, and societal ramifications. Therefore, effective congestion management is imperative, with traffic signal control (TSC) systems being pivotal in this endeavor. Conventional TSC systems, designed upon rule-based algorithms or reinforcement learning (RL), frequently exhibit deficiencies in managing the complexities and variabilities of urban traffic flows, constrained by their limited capacity for adaptation to unfamiliar scenarios. In response to these limitations, this work introduces …

arxiv capabilities control cs.lg environments human language language model large language large language model light llm signal traffic type urban

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