<ul><li>Bus holding control is a widely adopted strategy for maintaining stability and improving the operational efficiency of bus systems.</li><li>Traditional model-based methods face challenges with low accuracy of bus state prediction and passenger demand estimation.</li><li>Reinforcement Learning (RL) has demonstrated potential in formulating bus holding strategies.</li><li>This study introduces an automatic reward generation paradigm, LLM-enhanced RL, which improves reward functions using Large Language Models (LLMs).</li></ul>

Large Language Model-Enhanced Reinforcement Learning for Generic Bus Holding Control Strategies

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