HRSim: An agent-based simulation platform for high-capacity ride-sharing services
Wang Chen, Hongzheng Shi, Jintao Ke
TL;DR
HRSim addresses the need for scalable, open-source simulation of high-capacity ride-sharing by integrating real road networks and demand data with six modular components (infrastructure, pricing, routing, matching, repositioning, visualization) and employing ILP-based and heuristic methods. It demonstrates the matching and dispatch workflow with an objective and constraints suitable for large-scale evaluation, and showcases applications in carbon-emission quantification, city-scale scaling laws, and strategy analysis, including pricing and detour policies, under realistic conditions. The framework highlights how higher vehicle capacity and optimized strategies can improve service rates, occupancy, and emissions reductions, while also exposing potential revenue trade-offs. As an open testbed, HRSim enables policymakers and ride-sharing operators to design efficient, sustainable urban mobility systems and to prototype advanced algorithms, including RL-based approaches and future autonomous-vehicle integration.
Abstract
The rapid growth of ride-sharing services presents a promising solution to urban transportation challenges, such as congestion and carbon emissions. However, developing efficient operational strategies, such as pricing, matching, and fleet management, requires robust simulation tools that can replicate real-world dynamics at scale. Existing platforms often lack the capacity, flexibility, or open-source accessibility needed to support large-scale, high-capacity ride-sharing services. To address these gaps, we introduce HRSim, an open-source, agent-based High-capacity Ride-sharing Simulator. HRSim integrates real-world road networks and demand data to simulate dynamic ride-sharing operations, including pricing, routing, matching, and repositioning. Its module design supports both ride-sharing and solo-hailing service modes. Also, it includes a visualization module for real-time performance analysis. In addition, HRSim incorporates integer linear programming and heuristic algorithms, which can achieve large-scale simulations of high-capacity ride-sharing services. Applications demonstrate HRSim's utility in various perspectives, including quantifying carbon emissions, scaling ride-sharing performance, evaluating new strategies, etc. By bridging the gap between theoretical research and practical implementation, HRSim serves as a versatile testbed for policymakers and transportation network companies to optimize ride-sharing systems for efficiency and sustainability.