Chair for Network Dynamics, Institute for Theoretical Physics and Center for Advancing Electronics Dresden (cfaed), Technical University of Dresden, 01069 Dresden, Germany.
Network Dynamics, Max Planck Institute for Dynamics and Self-Organization (MPIDS), 37077 Göttingen, Germany.
Phys Rev Lett. 2020 Dec 11;125(24):248302. doi: 10.1103/PhysRevLett.125.248302.
Ride-sharing services may substantially contribute to future sustainable mobility. Their collective dynamics intricately depend on the topology of the underlying street network, the spatiotemporal demand distribution, and the dispatching algorithm. The efficiency of ride-sharing fleets is thus hard to quantify and compare in a unified way. Here, we derive an efficiency observable from the collective nonlinear dynamics and show that it exhibits a universal scaling law. For any given dispatcher, we find a common scaling that yields data collapse across qualitatively different topologies of model networks and empirical street networks from cities, islands, and rural areas. A mean-field analysis confirms this view and reveals a single scaling parameter that jointly captures the influence of network topology and demand distribution. These results further our conceptual understanding of the collective dynamics of ride-sharing fleets and support the evaluation of ride-sharing services and their transfer to previously unserviced regions or unprecedented demand patterns.
拼车服务可能会对未来的可持续交通做出重大贡献。它们的集体动态复杂地依赖于基础街道网络的拓扑结构、时空需求分布和调度算法。因此，拼车车队的效率很难以统一的方式进行量化和比较。在这里，我们从集体非线性动力学中推导出一个可观测的效率，并表明它表现出一种普遍的标度律。对于任何给定的调度器，我们发现了一种共同的标度，它在来自城市、岛屿和农村地区的不同性质的模型网络和实际街道网络的拓扑结构中产生了数据的崩溃。均值场分析证实了这一观点，并揭示了一个共同的标度参数，它共同捕捉了网络拓扑和需求分布的影响。这些结果进一步加深了我们对拼车车队集体动力学的理解，并支持对拼车服务的评估及其向以前未服务的地区或前所未有的需求模式的转移。
