# CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms

arXiv: https://arxiv.org/abs/1905.11353 · HTML: https://mlsanity.com/paper/1905.11353/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2019.

## Built on by (most cited first, 22 of 22)

- [Reinforcement Learning for Ridesharing: An Extended Survey](https://mlsanity.com/paper/2105.01099/) (2021)
- [Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning](https://mlsanity.com/paper/2103.04555/) (2021)
- [Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2102.07359/) (2021)
- [An End-to-End Reinforcement Learning Based Approach for Micro-View Order-Dispatching in Ride-Hailing](https://mlsanity.com/paper/2408.10479/) (2024)
- [i-Rebalance: Personalized Vehicle Repositioning for Supply Demand Balance](https://mlsanity.com/paper/2401.04429/) (2024)
- [Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing](https://mlsanity.com/paper/2405.13205/) (2024)
- [Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey](https://mlsanity.com/paper/2108.04462/) (2021)
- [A Survey of Machine Learning-Based Ride-Hailing Planning](https://mlsanity.com/paper/2303.14646/) (2023)
- [Mutual Information as Intrinsic Reward of Reinforcement Learning Agents for On-demand Ride Pooling](https://mlsanity.com/paper/2312.15195/) (2023)
- [A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications](https://mlsanity.com/paper/2210.14524/) (2022)
- [Vehicle Dispatching and Routing of On-Demand Intercity Ride-Pooling Services: A Multi-Agent Hierarchical Reinforcement Learning Approach](https://mlsanity.com/paper/2307.06742/) (2023)
- [LLM-ODDR: A Large Language Model Framework for Joint Order Dispatching and Driver Repositioning](https://mlsanity.com/paper/2505.22695/) (2025)
- [DROP: Deep relocating option policy for optimal ride-hailing vehicle repositioning](https://mlsanity.com/paper/2109.04149/) (2021)
- [DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2111.05670/) (2021)
- [Atomic Proximal Policy Optimization for Electric Robo-Taxi Dispatch and Charger Allocation](https://mlsanity.com/paper/2502.13392/) (2025)
- [Learning Model-Based Vehicle-Relocation Decisions for Real-Time Ride-Sharing: Hybridizing Learning and Optimization](https://mlsanity.com/paper/2105.13461/) (2021)
- [Learning Model Predictive Controllers for Real-Time Ride-Hailing Vehicle Relocation and Pricing Decisions](https://mlsanity.com/paper/2111.03204/) (2021)
- [A Survey on Applications of Reinforcement Learning in Spatial Resource Allocation](https://mlsanity.com/paper/2403.03643/) (2024)
- [Scalable Ride-Sourcing Vehicle Rebalancing with Service Accessibility Guarantee: A Constrained Mean-Field Reinforcement Learning Approach](https://mlsanity.com/paper/2503.24183/) (2025)
- [TrafficClaw: A Generalizable LLM Agent in the Unified Physical Environment for Urban Traffic Control](https://mlsanity.com/paper/2604.17456/) (2026)
- [Aggregate-then-Calibrate for Human-centered Assessment with Theoretical Guarantees](https://mlsanity.com/paper/2608.02455/) (2026)
- [GenMatch: An End-to-End Generative Matching Framework for Micro-View Order-Dispatching in Ride-Hailing](https://mlsanity.com/paper/2608.19751/) (2026)

## Abstract (arXiv)

How to optimally dispatch orders to vehicles and how to tradeoff between immediate and future returns are fundamental questions for a typical ride-hailing platform. We model ride-hailing as a large-scale parallel ranking problem and study the joint decision-making task of order dispatching and fleet management in online ride-hailing platforms. This task brings unique challenges in the following four aspects. First, to facilitate a huge number of vehicles to act and learn efficiently and robustly, we treat each region cell as an agent and build a multi-agent reinforcement learning framework. Second, to coordinate the agents from different regions to achieve long-term benefits, we leverage the geographical hierarchy of the region grids to perform hierarchical reinforcement learning. Third, to deal with the heterogeneous and variant action space for joint order dispatching and fleet management, we design the action as the ranking weight vector to rank and select the specific order or the fleet management destination in a unified formulation. Fourth, to achieve the multi-scale ride-hailing platform, we conduct the decision-making process in a hierarchical way where a multi-head attention mechanism is utilized to incorporate the impacts of neighbor agents and capture the key agent in each scale. The whole novel framework is named as CoRide. Extensive experiments based on multiple cities real-world data as well as analytic synthetic data demonstrate that CoRide provides superior performance in terms of platform revenue and user experience in the task of city-wide hybrid order dispatching and fleet management over strong baselines.