LLM-ODDR: A Large Language Model Framework for Joint Order Dispatching and Driver Repositioning

TL;DR

This paper introduces LLM-ODDR, a novel framework using large language models to improve order dispatching and driver repositioning in ride-hailing, emphasizing fairness, interpretability, and adaptability in dynamic urban environments.

Contribution

The paper presents the first use of LLMs for joint order dispatching and driver repositioning, integrating multi-objective evaluation, fairness, and demand-awareness for ride-hailing optimization.

Findings

Outperforms traditional methods in effectiveness and adaptability.

Enhances decision interpretability in ride-hailing operations.

Demonstrates robustness in real-world datasets from Manhattan.

Abstract

Ride-hailing platforms face significant challenges in optimizing order dispatching and driver repositioning operations in dynamic urban environments. Traditional approaches based on combinatorial optimization, rule-based heuristics, and reinforcement learning often overlook driver income fairness, interpretability, and adaptability to real-world dynamics. To address these gaps, we propose LLM-ODDR, a novel framework leveraging Large Language Models (LLMs) for joint Order Dispatching and Driver Repositioning (ODDR) in ride-hailing services. LLM-ODDR framework comprises three key components: (1) Multi-objective-guided Order Value Refinement, which evaluates orders by considering multiple objectives to determine their overall value; (2) Fairness-aware Order Dispatching, which balances platform revenue with driver income fairness; and (3) Spatiotemporal Demand-Aware Driver Repositioning, which optimizes idle vehicle placement based on historical patterns and projected supply. We also develop JointDR-GPT, a fine-tuned model optimized for ODDR tasks with domain knowledge. Extensive experiments on real-world datasets from Manhattan taxi operations demonstrate that our framework significantly outperforms traditional methods in terms of effectiveness, adaptability to anomalous conditions, and decision interpretability. To our knowledge, this is the first exploration of LLMs as decision-making agents in ride-hailing ODDR tasks, establishing foundational insights for integrating advanced language models within intelligent transportation systems.