RideGym: A Standardized Interface for Real-World Large-Scale Ride-Sharing System

2026-07-11

AI Summary

This work addresses the lack of standardized interfaces in existing ride-hailing simulation platforms, which undermines research reproducibility, introduces unfairness in algorithmic comparisons, and leads to redundant development efforts. To resolve these issues, the authors propose an open-source, Gym-style multi-agent reinforcement learning (MARL) simulation framework that, for the first time, offers a standardized, scalable, and algorithm-agnostic interface tailored to real-world city-scale ride-hailing systems. The framework incorporates realistic road networks, automated shortest-path routing, and flexible configuration mechanisms, supporting both MARL and model-driven approaches. It efficiently simulates thousands of vehicles and tens of thousands of ride requests within an hour-long scenario in under one minute. Empirical evaluations using the framework reveal that exploration noise significantly impacts both the performance and relative ranking of MARL algorithms.

Abstract

Ride-sharing has become an essential component of modern urban transportation and has attracted significant attention across computer science, transportation, and management science. While the field spans a broad range of problems, such as driver relocation, dynamic pricing, and vehicle charging or fueling dispatch, the core challenge remains order assignment and trip bundling, which directly affect urban traffic efficiency and carbon emissions. Despite its importance, existing simulation platforms are typically tailored to specific operational studies or tightly coupled to a particular dispatch algorithm, and rarely expose a standardized, learning-friendly interface. As a result, most researchers still build customized environments from scratch, raising serious concerns about reproducibility and fair comparison, and incurring substantial redundant effort. To address this gap, we present RideGym, the first open-source, standardized Gym-style interface tailored to MARL-based order dispatch in real-world ride-sharing systems. By fully decoupling the environment from the dispatch algorithm, RideGym enables diverse learning-based and model-based methods to be developed and compared under identical, fully specified conditions. It supports efficient, large-scale city-level simulations on real road networks, and offers flexible configurations for vehicle attributes, order specifications, and automatic shortest-path routing. We validate RideGym by reproducing several baselines, and demonstrate its high efficiency, with a one-hour simulation involving thousands of vehicles and tens of thousands of orders completed within one minute across all methods. Moreover, we reveal that the choice of exploration noise can significantly affect both the performance and the relative ranking of MARL solutions, an aspect often overlooked in prior work.

Problem

• ride-sharing
• order assignment
• simulation platform
• reproducibility
• standardized interface

Innovation

• RideGym
• multi-agent reinforcement learning (MARL)
• order dispatch
• standardized simulation interface
• large-scale ride-sharing