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
Cities are complex systems, composed of people, places, flows, and activities (Batty, 2013)
Bike sharing schemes can play important role in examining the “first/last mile” problem. This is the distance between home/workplace and public transport that is too far to walk (Fishman, 2016; Saberi, Ghamami, Gu, Shojaei, & Fishman, 2018; Shaheen, Guzman, & Zhang, 2010), and bike schemes provide access to other forms of public transport and mass transit: they act as the “capillaries” for the mass transit aorta
Its average value shows an increase after the new metro opening, suggesting that the dockless bike trip network became more locally connected. These results suggest that the dockless bike sharing mobility network became denser and more heterogeneous after the opening of the new metro service
Summary
Cities are complex systems, composed of people, places, flows, and activities (Batty, 2013). Zhong, Arisona, Huang, Batty, and Schmitt (2014) used smart card data (bus and metro) and graph-based approaches to quantify the dynamics of urban structures through the analysis of spatial networks This characterizes by medium-long distance travel but fails to reveal dynamics in local areas over short distances. Some research has used cell phone data to detect urban travel flows and some aspects of urban structure (e.g. home-to-work commuting structures) (Calabrese, Di Lorenzo, Liu, & Ratti, 2011; Louail et al, 2014), but this lacks spatial detail due to cellular positioning, with median errors of 599 m (Zandbergen, 2009) This results in large uncertainties when inferring people's movement over shorter distances. The advent of dockless bike schemes opens up the opportunity to examine the last mile in detail
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