On-demand ride services or ride-sourcing services have been experiencing fast development in the past decade. Various mathematical models and optimization algorithms have been developed to help ride-sourcing platforms design operational strategies with higher efficiency. However, due to cost and reliability issues (implementing an immature algorithm for real operations may result in system turbulence), it is commonly infeasible to validate these models and train/test these optimization algorithms within real-world ride sourcing platforms. Acting as a useful test bed, a simulation platform for ride-sourcing systems will be very important to conduct algorithm training/testing or model validation through trails and errors. While previous studies have established a variety of simulators for their own tasks, it lacks a fair and public platform for comparing the models or 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 different tasks they can implement. To address the challenges, we propose a novel multi-functional and open-sourced simulation platform for ride-sourcing systems, which can simulate the behaviors and movements of various agents on a real transportation network. It provides a few accessible portals for users 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. In addition, it can be used to test how well the theoretical models approximate the simulated outcomes. 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.

翻译：按需出行服务（或称网约车服务）在过去十年中经历了快速发展。为了帮助网约车平台设计更高效的运营策略，各类数学模型与优化算法已被广泛开发。然而，由于成本与可靠性问题（将不成熟的算法直接应用于实际运营可能引发系统动荡），在真实网约车平台中验证这些模型并训练/测试优化算法通常不可行。作为有效的试验平台，网约车系统仿真平台对于通过试错方式开展算法训练、测试或模型验证具有重要意义。尽管已有研究为各自任务建立了多种仿真器，但缺乏一个公平且公开的平台来比较不同研究者提出的模型或算法。此外，现有仿真器仍面临诸多挑战，包括其与真实网约车系统环境的接近程度，以及所能实现任务的完整性。为应对这些挑战，我们提出了一种新颖的多功能开源网约车系统仿真平台，该平台能够在真实交通网络上模拟多种智能体的行为与移动。平台提供了多个可访问的接口，使用户能够训练和测试各类优化算法（特别是强化学习算法），以完成包括即时匹配、空车重定位和动态定价在内的多种任务。此外，该平台还可用于检验理论模型对仿真结果的近似程度。基于真实世界数据的实验评估表明，该仿真器能够高效、有效地充当与按需出行服务运营相关的多种任务的试验平台。