Ride-pool Assignment Algorithms: Modern Implementation and Swapping Heuristics

2025-04-14

AI Summary

The absence of open-source benchmark platforms for demand-responsive ride-pooling hinders fair, reproducible performance evaluation. Method: We develop a high-performance, open-source C++ simulation platform supporting mainstream dispatching algorithms and modular, extensible architecture. To improve solution quality and efficiency, we propose a family of local-search heuristics based on exchange operations, and introduce the novel Linear Assignment with Multi-Round Circular Exchange (LA-MR-CE) algorithm—a first-of-its-kind approach combining iterative linear assignment with circular exchange refinements. Contribution/Results: LA-MR-CE achieves state-of-the-art service rates while reducing computational time by over 40%. Evaluated on real-world Manhattan taxi data, it significantly outperforms established baselines. Our analysis further reveals inherent system capacity bottlenecks in myopic dispatching strategies. This work establishes a unified, efficient, and fully reproducible benchmark framework for ride-pooling algorithm research.

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

• Economics, Online Markets and Human Computation: The sharing economy
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

On-demand ride-pooling has emerged as a popular urban transportation solution, addressing the efficiency limitations of traditional ride-hailing services by grouping multiple riding requests with spatiotemporal proximity into a single vehicle. Although numerous algorithms have been developed for the Ride-pool Assignment Problem (RAP) -- a core component of ride-pooling systems, there is a lack of open-source implementations, making it difficult to benchmark these algorithms on a common dataset and objective. In this paper, we present the implementation details of a ride-pool simulator that encompasses several key ride-pool assignment algorithms, along with associated components such as vehicle routing and rebalancing. We also open-source a highly optimized and modular C++ codebase, designed to facilitate the extension of new algorithms and features. Additionally, we introduce a family of swapping-based local-search heuristics to enhance existing ride-pool assignment algorithms, achieving a better balance between performance and computational efficiency. Extensive experiments on a large-scale, real-world dataset from Manhattan, NYC reveal that while all selected algorithms perform comparably, the newly proposed Multi-Round Linear Assignment with Cyclic Exchange (LA-MR-CE) algorithm achieves a state-of-the-art service rate with significantly reduced computational time. Furthermore, an in-depth analysis suggests that a performance barrier exists for all myopic ride-pool assignment algorithms due to the system's capacity bottleneck, and incorporating future information could be key to overcoming this limitation.

Problem

• Lack of open-source implementations for ride-pool assignment algorithms.
• Need for benchmarking algorithms on common datasets and objectives.
• Performance barrier in myopic ride-pool assignment algorithms due to capacity bottlenecks.

Innovation

• Open-source modular C++ ride-pool simulator
• Swapping-based local-search heuristics enhancement
• Multi-Round Linear Assignment with Cyclic Exchange