Optimizing Ride-Pooling Operations with Extended Pickup and Drop-Off Flexibility

2025-03-11

AI Summary

Existing ride-pooling systems assume fixed pickup and drop-off points, limiting matching efficiency and dispatching potential. This paper proposes a real-time ride-pooling matching framework supporting elastic pickup and drop-off zones, wherein passenger-walkable areas are modeled as optimization variables—thereby relaxing conventional point-to-point constraints. The method integrates tree-structured matching generation, spatiotemporally constrained joint path optimization, and a dynamic request-vehicle assignment mechanism. A city-scale simulation framework is built upon real-world taxi trajectory data for rigorous evaluation. Experimental results on large-scale real datasets demonstrate that, compared to state-of-the-art approaches, the proposed framework increases served requests by up to 13% and reduces average vehicle travel distance by up to 21%, significantly enhancing system capacity and operational efficiency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

The Ride-Pool Matching Problem (RMP) is central to on-demand ride-pooling services, where vehicles must be matched with multiple requests while adhering to service constraints such as pickup delays, detour limits, and vehicle capacity. Most existing RMP solutions assume passengers are picked up and dropped off at their original locations, neglecting the potential for passengers to walk to nearby spots to meet vehicles. This assumption restricts the optimization potential in ride-pooling operations. In this paper, we propose a novel matching method that incorporates extended pickup and drop-off areas for passengers. We first design a tree-based approach to efficiently generate feasible matches between passengers and vehicles. Next, we optimize vehicle routes to cover all designated pickup and drop-off locations while minimizing total travel distance. Finally, we employ dynamic assignment strategies to achieve optimal matching outcomes. Experiments on city-scale taxi datasets demonstrate that our method improves the number of served requests by up to 13% and average travel distance by up to 21% compared to leading existing solutions, underscoring the potential of leveraging passenger mobility to significantly enhance ride-pooling service efficiency.

Problem

• Optimizing ride-pooling with flexible pickup and drop-off locations.
• Enhancing matching efficiency by allowing passengers to walk to nearby spots.
• Reducing travel distance and increasing served requests in ride-pooling services.

Innovation

• Extended pickup and drop-off flexibility
• Tree-based approach for efficient matching
• Dynamic assignment for optimal outcomes