A spatiotemporal and graph-based analysis of dockless bike sharing patterns to understand urban flows over the last mile
A spatiotemporal and graph-based analysis of dockless bike sharing patterns to understand urban flows over the last mile
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通讯作者：
Yuanxuan Yang;Alison J. Heppenstall;Andy Turner;Alexis J. Comber
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作者：
Yuanxuan Yang;Alison J. Heppenstall;Andy Turner;Alexis J. Comber
最近出现的无码头自行车共享系统带来了新的城市交通模式。用户可以从他们的起点和目的地位置而不是对接站开始开始和结束行程。分析这些自行车的时空可用性的变化能够以比分析旅行卡或基于码头的自行车计划数据更细的粒度提供对城市动态的洞察。这项研究分析了中国南昌市新地铁线路投入运营期间的无桩共享自行车。它使用空间统计和基于图形的方法来量化出行行为的变化，并生成以前无法获得的关于城市道路结构的见解。地理统计学分析支持对时空旅行行为的大规模变化的理解，基于图形的方法允许艾德和表征各个位置之间的本地旅行流的变化。结果显示，新的地铁服务如何促进附近的自行车需求，但有相当大的空间变化，并改变了时空模式的自行车出行行为。该分析还量化了出行流程结构的演变，表明了无桩自行车计划的弹性及其适应出行行为变化的能力。更广泛地说，这项研究展示了如何通过对无桩自行车数据的分析来增强对“最后一英里”城市动态的理解。这些允许不同的交通系统之间的局部时空相互依赖性的变化进行评估，并支持空间详细的城市和交通规划。艾德确定了一些需要进一步开展工作的领域，以便更好地了解不同交通系统组件之间的相互依赖关系。
The recent emergence of dockless bike sharing systems has resulted in new patterns of urban transport. Users can begin and end trips from their origin and destination locations rather than docking stations. Analysis of changes in the spatiotemporal availability of such bikes has the ability to provide insights into urban dynamics at a fi ner granularity than is possible through analysis of travel card or dock-based bike scheme data. This study analyses dockless bike sharing in Nanchang, China over a period when a new metro line came into operation. It uses spatial statistics and graph-based approaches to quantify changes in travel behaviours and generates previously unobtainable insights about urban fl ow structures. Geostatistical analyses support understanding of large-scale changes in spatiotemporal travel behaviours and graph-based approaches allow changes in local travel fl ows between individual locations to be quanti fi ed and characterized. The results show how the new metro service boosted nearby bike demand, but with considerable spatial variation, and changed the spatiotemporal patterns of bike travel behaviour. The analysis also quanti fi es the evolution of travel fl ow structures, indicating the resilience of dockless bike schemes and their ability to adapt to changes in travel behaviours. More widely, this study demonstrates how an enhanced understanding of urban dynamics over the “ last-mile ” is supported by the analyses of dockless bike data. These allow changes in local spatiotemporal interdependencies between di ff erent transport systems to be evaluated, and support spatially detailed urban and transport planning. A number of areas of further work are identi fi ed to better to understand interdependencies between di ff erent transit system components.