Ride-pool Assignment Algorithms: Modern Implementation and Swapping Heuristics

TL;DR

This work provides a modern, open-source simulation platform for centralized ride-pool assignment and introduces a family of swapping-based local-search heuristics that improve epoch-wise decision quality without sacrificing scalability. By comparing exact, column-generation, linear-assignment, and several swapping variants on real Manhattan data, the authors demonstrate that LA-MR-CE delivers state-of-the-art service rates with substantially reduced computation time. A key finding is the existence of a throughput bottleneck in myopic RAP, with future information (e.g., 8 minutes ahead) significantly boosting service rates for both full and swapping-based methods. The results highlight the practical value of incorporating foresight into ride-pool systems and provide a modular, extensible toolchain for benchmarking new algorithms.

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.

Figures (11)

• Figure 1: A schematic diagram illustrating the algorithm
• Figure 2: A schematic diagram illustrating the algorithm
• Figure 3: An example illustrating the defect of naive swapping on dependent vehicles. In Figure (a), dashed edges represent the current assignment while solid edges represent the valid naive swaps. The numbers next to the solid edges indicate cost reduction. In Figure (b), the same problem is shown where squares are vehicles while circles denote request origin-destination pairs. The numbers next to the edges indicate the distances. Although both naive swaps reduce costs on their own, applying both simultaneously leads to a worse assignment.
• Figure 4: A schematic diagram illustrating the algorithm
• Figure 5: The pattern of cyclic exchange in the partition problem

Theorems & Definitions (6)

• Definition 5.1: Independent Requests
• Definition 5.2: Naive Swap
• Definition 5.3: Independent Vehicles
• Definition 5.4: Proper Swap
• Definition 5.5: Valid Cycle
• Definition 6.1: Stable Heuristic