无桩共享单车系统网络扩展的基于图的优化
Graph-Based Optimisation of Network Expansion in a Dockless Bike Sharing System
AI总结：
本文基于 Moby Bikes 出行数据构建优化地理-时间图，结合 Louvain 社区检测揭示共享单车需求的时空使用模式，为无桩共享单车系统扩展选址和运营再分配效率提升提供依据。
摘要：
共享单车系统（BSSs）已在全球一千多个城市部署，并在许多城市交通系统中发挥着重要作用。共享单车系统能够缓解拥堵、减少污染并促进体育锻炼。为了优化系统运营效率，探索共享单车需求的时空模式以及影响这些模式的因素至关重要。本研究利用无桩共享单车运营商 Moby Bikes 的出行数据构建了一个优化的地理-时间图。图的优化过程揭示了未来共享单车系统扩展中设置新站点的最佳位置。采用社区检测技术 Louvain 算法来揭示不同时间粒度下的使用模式。社区检测结果表明，存在大量自包含的子网络，它们在各自的时间粒度级别上表现出相似的使用模式。总体而言，本研究进一步证实共享单车系统本质上是时空系统，社区的存在由时空动态驱动。这些发现可能有助于运营商提高再分配效率。
英文摘要：
Bike-sharing systems (BSSs) are deployed in over a thousand cities worldwide and play an important role in many urban transportation systems. BSSs alleviate congestion, reduce pollution and promote physical exercise. It is essential to explore the spatiotemporal patterns of bike-sharing demand, as well as the factors that influence these patterns, in order to optimise system operational efficiency. In this study, an optimised geo-temporal graph is constructed using trip data from Moby Bikes, a dockless BSS operator. The process of optimising the graph unveiled prime locations for erecting new stations during future expansions of the BSS. The Louvain algorithm, a community detection technique, is employed to uncover usage patterns at different levels of temporal granularity. The community detection results reveal largely self-contained sub-networks that exhibit similar usage patterns at their respective levels of temporal granularity. Overall, this study reinforces that BSSs are intrinsically spatiotemporal systems, with community presence driven by spatiotemporal dynamics. These findings may aid operators in improving redistribution efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
