Technical Summary: LLM-ODDR

Problem Definition

Ride-hailing platforms face complex, real-time decision-making challenges in Order Dispatching (matching riders to drivers) and Driver Repositioning (strategically relocating idle vehicles). Traditional approaches, including combinatorial optimization, rule-based heuristics, and Reinforcement Learning (RL), often struggle with the dynamic nature of urban environments. Specifically, existing methods frequently:

• Treat dispatching and repositioning as separate problems, ignoring their inherent interdependence.
• Optimize for single objectives (e.g., immediate revenue), failing to balance competing goals like platform revenue, passenger wait times, and driver income fairness.
• Lack interpretability in their decision-making processes ("black-box" nature).
• Exhibit limited adaptability to anomalous conditions (e.g., surge events) and require extensive parameter tuning for different scenarios.

The authors propose that Large Language Models (LLMs) offer a paradigm shift by leveraging their contextual reasoning, ability to synthesize diverse inputs, and capacity for multi-dimensional reasoning to address these gaps.

Methodology: The LLM-ODDR Framework

The paper introduces LLM-ODDR, a novel framework that utilizes LLMs as decision-making agents for joint Order Dispatching and Driver Repositioning (ODDR). The framework consists of three core components and a specialized fine-tuned model:

1. Multi-objective-guided Order Value Refinement

This module evaluates the overall value of each ride request by integrating immediate rewards with future area value. It employs an iterative self-refinement mechanism:

• Order Value Scorer: Uses a tailored prompt to evaluate orders based on multiple constraints: waiting time urgency, immediate reward, and the "future advantage value" of the destination area (derived from historical order density).
• Order Value Reviewer: Systematically evaluates the reasonableness of the scorer's assessments. If an order's valuation is deemed unreasonable, the reviewer provides specific feedback, and the scorer refines the valuation in the next iteration. This loop continues until a stopping condition (maximum iterations or consensus) is met.

2. Fairness-aware Order Dispatching

Once order values are refined, the framework assigns orders to drivers. Unlike traditional matching algorithms that focus solely on efficiency, this module incorporates driver income fairness as a primary optimization objective.

• The LLM considers driver attributes (current earnings, historical orders, idle time) alongside order values.
• The prompt explicitly instructs the model to prioritize fairness, giving preference to drivers with lower historical earnings to ensure equitable income distribution across the fleet.

3. Spatiotemporal Demand-Aware Driver Repositioning

This module optimizes the placement of idle vehicles based on the outcomes of the dispatching phase and historical patterns.

• It integrates three spatiotemporal factors for candidate regions: Historical Demand (order requests), Historical Matches (fulfillment capacity), and Future Vehicle Arrivals (projected supply based on current dispatch decisions).
• The LLM analyzes these inputs to identify regions with high potential demand and insufficient supply, directing idle drivers to these areas to improve future matching rates.

4. JointDR-GPT

To enhance performance, the authors developed JointDR-GPT, a model fine-tuned from Llama 3.1-70B using the LoRA (Low-Rank Adaptation) strategy. It is trained via hybrid instruction fine-tuning on datasets generated by GPT-4, cA2C, and TVal, specifically optimized for the joint ODDR tasks.

Key Contributions

1. First LLM-based ODDR Framework: To the authors' knowledge, this is the first exploration of LLMs as decision-making agents for the joint optimization of order dispatching and driver repositioning.
2. Novel Framework Design: The introduction of the three-component architecture (Value Refinement, Fairness-aware Dispatching, and Demand-aware Repositioning) that explicitly addresses multi-objective trade-offs and interpretability.
3. Specialized Model (JointDR-GPT): The development of a fine-tuned model that significantly outperforms general-purpose LLMs and traditional methods in this domain.
4. Empirical Validation: Extensive experiments on real-world Manhattan taxi datasets demonstrating superior effectiveness, adaptability to surge events, and decision interpretability.

Experimental Results

The framework was evaluated on three datasets (Small-100, Small-200, Large-500) derived from Manhattan taxi records, comparing JointDR-GPT against traditional baselines (KM, TVal, cA2C, GRC) and various LLMs (GPT-4o, Llama3.1, Qwen2.5).

• Performance Metrics: The study used Gross Merchandise Volume (GMV) and Order Response Rate (ORR).
• Comparative Performance: JointDR-GPT consistently outperformed all baselines. On the Small-100 dataset, it achieved a GMV improvement of 38.47% over the KM baseline and 4.61% over the best-performing GPT-4o.
• Ablation Studies: Removing any of the three core components (scorer, LLM-dispatching, or LLM-repositioning) resulted in performance degradation, confirming the necessity of the integrated approach.
• Fairness: The framework demonstrated superior ability to balance driver income, achieving higher median driver income compared to other models while maintaining high GMV.
• Surge Adaptability: In case studies simulating demand surges (e.g., concerts), JointDR-GPT showed greater adaptability and order completion rates compared to traditional RL and heuristic methods, which struggled to perceive and react to rapid environmental changes.

Significance and Claims

The paper claims that LLM-ODDR establishes foundational insights for integrating advanced language models into Intelligent Transportation Systems (ITS). Its significance lies in:

• Holistic Optimization: Successfully balancing competing stakeholder interests (platform revenue, driver fairness, passenger wait times) without rigid formulations.
• Interpretability: Transforming opaque algorithmic decisions into transparent, explainable reasoning chains, which builds stakeholder trust.
• Adaptability: Demonstrating the ability to reason about anomalous events with limited historical data, leveraging pre-trained knowledge to generalize across scenarios.

The authors acknowledge that LLM-based decision-making currently incurs higher computational costs and latency (17–30 seconds per cycle) compared to traditional methods. However, they argue that techniques such as parallel decomposition and model distillation can mitigate these issues, making practical deployment a realistic near-term prospect. They position this work as a step toward more sustainable, fair, and interpretable ride-hailing systems.