Scalable Ride-Sourcing Vehicle Rebalancing with Service Accessibility Guarantee: A Constrained Mean-Field Reinforcement Learning Approach

• URL: http://arxiv.org/abs/2503.24183v2
• Date: Fri, 23 May 2025 01:44:38 GMT
• Title: Scalable Ride-Sourcing Vehicle Rebalancing with Service Accessibility Guarantee: A Constrained Mean-Field Reinforcement Learning Approach
• Authors: Matej Jusup, Kenan Zhang, Zhiyuan Hu, Barna Pásztor, Andreas Krause, Francesco Corman,
• Abstract summary: We introduce continuous-state mean-field control (MFC) and mean-field reinforcement learning (MFRL) models that employ continuous vehicle repositioning actions.<n>MFC and MFRL offer scalable solutions by modeling each vehicle's behavior through interaction with the vehicle distribution, rather than with individual vehicles.<n>Our approach scales to tens of thousands of vehicles, with training times comparable to the decision time of a single linear programming rebalancing.
• Score: 42.070187224580344
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid expansion of ride-sourcing services such as Uber, Lyft, and Didi Chuxing has fundamentally reshaped urban transportation by offering flexible, on-demand mobility via mobile applications. Despite their convenience, these platforms confront significant operational challenges, particularly vehicle rebalancing - the strategic repositioning of a large group of vehicles to address spatiotemporal mismatches in supply and demand. Inadequate rebalancing not only results in prolonged rider waiting times and inefficient vehicle utilization but also leads to fairness issues, such as the inequitable distribution of service quality and disparities in driver income. To tackle these complexities, we introduce continuous-state mean-field control (MFC) and mean-field reinforcement learning (MFRL) models that employ continuous vehicle repositioning actions. MFC and MFRL offer scalable solutions by modeling each vehicle's behavior through interaction with the vehicle distribution, rather than with individual vehicles. This limits the issues arising from the curse of dimensionality inherent in traditional multi-agent methods, enabling coordination across large fleets with significantly reduced computational complexity. To ensure equitable service access across geographic regions, we integrate an accessibility constraint into both models. Extensive empirical evaluation using real-world data-driven simulation of Shenzhen demonstrates the real-time efficiency and robustness of our approach. Remarkably, it scales to tens of thousands of vehicles, with training times comparable to the decision time of a single linear programming rebalancing. Besides, policies generated by our approach effectively explore the efficiency-equity Pareto front, outperforming conventional benchmarks across key metrics like fleet utilization, fulfilled requests, and pickup distance, while ensuring equitable service access.

Related papers

• Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework [79.088116316919]
  Connected Autonomous Vehicles (CAVs) have begun to open road testing around the world, but their safety and efficiency performance in complex scenarios is still not satisfactory. This paper proposes CoDrivingLLM, an interactive and learnable LLM-driven cooperative driving framework.
  arXiv  Detail & Related papers  (2024-09-19T14:36:00Z)
• GARLIC: GPT-Augmented Reinforcement Learning with Intelligent Control for Vehicle Dispatching [81.82487256783674]
  GARLIC: a framework of GPT-Augmented Reinforcement Learning with Intelligent Control for vehicle dispatching.<n>This paper introduces GARLIC: a framework of GPT-Augmented Reinforcement Learning with Intelligent Control for vehicle dispatching.
  arXiv  Detail & Related papers  (2024-08-19T08:23:38Z)
• Shared learning of powertrain control policies for vehicle fleets [3.9325957466009203]
  Deep reinforcement learning (DRL) aims at on-the-field learning of powertrain control policies that optimize fuel economy and other performance metrics. We present a novel framework for shared learning among a fleet of vehicles through the use of a distilled group policy. We find a fleet average improvement of 8.5 percent in fuel economy compared to the baseline.
  arXiv  Detail & Related papers  (2024-04-27T13:01:05Z)
• A Reinforcement Learning Approach for Dynamic Rebalancing in Bike-Sharing System [11.237099288412558]
  Bike-Sharing Systems provide eco-friendly urban mobility, contributing to the alleviation of traffic congestion and healthier lifestyles. Devising effective rebalancing strategies using vehicles to redistribute bikes among stations is therefore of uttermost importance for operators. This paper introduces atemporal reinforcement learning algorithm for the dynamic rebalancing problem with multiple vehicles.
  arXiv  Detail & Related papers  (2024-02-05T23:46:42Z)
• Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System [7.531863938542706]
  The rapid growth of the ride-hailing industry has revolutionized urban transportation worldwide. Despite its benefits, equity concerns arise as underserved communities face limited accessibility to affordable ride-hailing services. This paper focuses on enhancing both algorithmic and rider fairness through a novel vehicle rebalancing method.
  arXiv  Detail & Related papers  (2023-12-29T23:02:34Z)
• Safe Model-Based Multi-Agent Mean-Field Reinforcement Learning [48.667697255912614]
  Mean-field reinforcement learning addresses the policy of a representative agent interacting with the infinite population of identical agents. We propose Safe-M$3$-UCRL, the first model-based mean-field reinforcement learning algorithm that attains safe policies even in the case of unknown transitions. Our algorithm effectively meets the demand in critical areas while ensuring service accessibility in regions with low demand.
  arXiv  Detail & Related papers  (2023-06-29T15:57:07Z)
• Scalable Vehicle Re-Identification via Self-Supervision [66.2562538902156]
  Vehicle Re-Identification is one of the key elements in city-scale vehicle analytics systems. Many state-of-the-art solutions for vehicle re-id mostly focus on improving the accuracy on existing re-id benchmarks and often ignore computational complexity. We propose a simple yet effective hybrid solution empowered by self-supervised training which only uses a single network during inference time.
  arXiv  Detail & Related papers  (2022-05-16T12:14:42Z)
• Supervised Permutation Invariant Networks for Solving the CVRP with Bounded Fleet Size [3.5235974685889397]
  Learning to solve optimization problems, such as the vehicle routing problem, offers great computational advantages. We propose a powerful supervised deep learning framework that constructs a complete tour plan from scratch while respecting an apriori fixed number of vehicles. In combination with an efficient post-processing scheme, our supervised approach is not only much faster and easier to train but also competitive results.
  arXiv  Detail & Related papers  (2022-01-05T10:32:18Z)
• A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problem [2.299872239734834]
  We propose a modular framework for fleet rebalancing based on model-free reinforcement learning (RL) We formulate RL state and action spaces as distributions over a grid of the operating area, making the framework scalable. Numerical experiments, using real-world trip and network data, demonstrate that this approach has several distinct advantages over baseline methods.
  arXiv  Detail & Related papers  (2021-05-27T16:32:28Z)
• Value Function is All You Need: A Unified Learning Framework for Ride Hailing Platforms [57.21078336887961]
  Large ride-hailing platforms, such as DiDi, Uber and Lyft, connect tens of thousands of vehicles in a city to millions of ride demands throughout the day. We propose a unified value-based dynamic learning framework (V1D3) for tackling both tasks.
  arXiv  Detail & Related papers  (2021-05-18T19:22:24Z)
• Equilibrium Inverse Reinforcement Learning for Ride-hailing Vehicle Network [1.599072005190786]
  We formulate the problem of passenger-vehicle matching in a sparsely connected graph. We propose an algorithm to derive an equilibrium policy in a multi-agent environment.
  arXiv  Detail & Related papers  (2021-02-13T03:18:44Z)
• Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learning [101.64598586454571]
  A novel framework is proposed to allow reinforcement learning-based vehicular association, resource block (RB) allocation, and content selection of cooperative perception messages (CPMs) A federated RL approach is introduced in order to speed up the training process across vehicles. Results show that federated RL improves the training process, where better policies can be achieved within the same amount of time compared to the non-federated approach.
  arXiv  Detail & Related papers  (2020-12-07T02:09:15Z)
• A Distributed Model-Free Ride-Sharing Approach for Joint Matching, Pricing, and Dispatching using Deep Reinforcement Learning [32.0512015286512]
  We present a dynamic, demand aware, and pricing-based vehicle-passenger matching and route planning framework. Our framework is validated using the New York City Taxi dataset. Experimental results show the effectiveness of our approach in real-time and large scale settings.
  arXiv  Detail & Related papers  (2020-10-05T03:13:47Z)
• Real-time and Large-scale Fleet Allocation of Autonomous Taxis: A Case Study in New York Manhattan Island [14.501650948647324]
  Traditional models fail to efficiently allocate the available fleet to deal with the imbalance of supply (autonomous taxis) and demand (trips) We employ a Constrained Multi-agent Markov Decision Processes (CMMDP) to model fleet allocation decisions. We also leverage a Column Generation algorithm to guarantee the efficiency and optimality in a large scale.
  arXiv  Detail & Related papers  (2020-09-06T16:00:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.