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

2023-03-22 06:25:19
Siyuan Feng, Taijie Chen, Yuhao Zhang, Jintao Ke, Hai Yang

Abstract

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.

Abstract (translated)

过去几年中,实时骑行服务或骑行找车服务正在经历快速发展。开发了许多数学模型和优化算法,以帮助骑行找车平台以更高效的方式设计 operational strategies。然而,由于成本和可靠性问题(在真实业务中实施不成熟算法可能导致系统动荡),在现实世界骑行找车平台上验证这些模型和训练/测试这些优化算法通常是不太可能的。作为一个有用的测试平台,骑行找车系统的模拟平台非常重要,可以通过 trails and errors 进行算法培训和测试或模型验证。虽然在先前的研究中已经建立了许多不同的模拟工具,但它们缺乏一个公正的公共平台,以比较不同研究人员提出的模型或算法。此外,现有的模拟工具仍然面临许多挑战,包括它们与真实骑行找车系统环境的接近程度,以及它们可以执行的不同任务的完整程度。为了应对这些挑战,我们提出了一个新的多功能、开源的骑行找车系统模拟平台,它可以模拟真实交通网络中的各种代理的行为和移动。它提供了几个可用的门户,供用户训练和测试各种优化算法,特别是强化学习算法,以各种任务,包括实时匹配、闲置车辆重新定位和动态定价。此外,它还可以用于测试理论模型如何近似模拟结果。根据基于实际数据的实验评估,模拟器表明它是与实时骑行服务操作相关的各种任务的有效和高效的测试平台。

URL

https://arxiv.org/abs/2303.12336

PDF

https://arxiv.org/pdf/2303.12336.pdf


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