A multi-functional simulation platform for on-demand ride service operations

On-demand ride services or ride-sourcing services have been experiencing fast development and steadily reshaping the way people travel in the past decade. Various optimization algorithms, including reinforcement learning approaches, have been developed to help ride-sourcing platforms design better operational strategies to achieve higher efficiency. However, due to cost and reliability issues, it is commonly infeasible to validate these models and train/test these optimization algorithms within real-world ride-sourcing platforms. Acting as a proper test bed, a simulation platform for ride-sourcing systems will thus be essential for both researchers and industrial practitioners. While previous studies have established simulators for their tasks, they lack a fair and public platform for comparing the models/algorithms proposed by different researchers. In addition, the existing simulators still face many challenges, ranging from their closeness to real environments of ride-sourcing systems to the completeness of tasks they can implement. To address the challenges, we propose a novel simulation platform for ride-sourcing systems on real transportation networks. It provides a few accessible portals to train and test various optimization algorithms, especially reinforcement learning algorithms, for a variety of tasks, including on-demand matching, idle vehicle repositioning, and dynamic pricing. Evaluated on real-world data-based experiments, the simulator is demonstrated to be an efficient and effective test bed for various tasks related to on-demand ride service operations. • Develop a comprehensive, multi-functional, and open-sourced simulator for ride-sourcing. • The simulator is designed based on the transportation network, can consider heterogeneous behaviors of passengers and drivers. • The simulator has a multi-functional visualization module to present the simulated ride-sourcing market. • The simulator provides interfaces for the testing of optimization algorithms and reinforcement learning-based approaches. • A series of experiments are conducted to validate the effectiveness and efficiency of the proposed simulation platform.