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A new framework, LLM-ODDR, leverages Large Language Models to improve order dispatching and driver repositioning in ride-hailing services by balancing platform revenue, driver fairness, and demand patterns. Experiments with real-world data from Manhattan demonstrate that LLM-ODDR outperforms traditional methods in effectiveness, adaptability, and interpretability, marking a pioneering use of LLMs in transportation decision-making.