A spatiotemporal and graph-based analysis of dockless bike sharing patterns to understand urban flows over the last mile. (September 2019)

• Record Type: Journal Article
• Authors: Yang, Yuanxuan; Heppenstall, Alison; Turner, Andy; Comber, Alexis
• Is Part Of: Computers, environment and urban systems. Volume 77(2019)
• Publication Date: 2019-09
• Subjects: Dockless bikes -- Urban dynamics -- Spatial analysis -- Graph structure -- Transportation; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2019.101361
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: 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 finer 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 flow structures. Geostatistical analyses support understanding of large-scale changes in spatiotemporal travel behaviours and graph-based approaches allow changes in local travel flows between individual locations to be quantified 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 quantifies the evolution of travel flow 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 different transport systems to be evaluated, and support spatially detailed urban and transport planning. A number of areas of further work are identified to better to understand interdependencies between different transit system components. Highlights: Dockless bike data are used to analyse changes behaviours changes over the "last mile". New metro lines increased bike demand near to stations, but with considerable spatial variation New bike-plus-metro hubs were identified. Spatial analysis identified large scale behaviour changes and graph metrics quantified local scale changes The methods quantify urban flow structures, their evolution and dockless bike scheme resilience to infrastructure change.