Citation

Zhuang D, Jin JG, Shen Y, Jiang W. Int. J. Sustain. Transp. 2020; 15(2): 111-123.

Copyright

(Copyright © 2020, Informa - Taylor and Francis Group)

DOI

10.1080/15568318.2019.1699209

PMID

unavailable

Abstract

As a sustainable transportation alternative, cycling has been developed by many large cities as one essential measure for addressing the "last mile problem" in urban areas with high mobility demand. Developing a safe and friendly lane network for cycling has become an urgent task for governments, especially for many Chinese cities encountering a rapid increase in the use of "dockless" shared bikes, such as Mobike and Ofo. The emergence of such kind of app-driven dockless bike sharing systems results in a fast growth in the cycling mobility demand, and leads to a gap between the growing demand and the existing cycling infrastructure. Therefore, it is imperative to have a good understanding of the cycling travel demand and its infrastructure, and in turn to bridge the supply-demand gap. In this paper, we employ data mining techniques including graphic clustering algorithm, Louvain Method, on a large data set from one of the largest bike sharing companies in China. Then we applied the methodology in two cases in Shanghai, including a campus and an urban area. Typical cycling patterns in the spatial and temporal dimensions are identified automatically by the cluster analysis. It is also found that the construction of the cycling infrastructure is closely related to three factors: non-negligible impact of points of interest (POIs), geographical barriers, and temporal variance of the network. Managerial insights and policy measures are proposed accordingly for improving the cycle lane network.

Language: en

Keywords

Cycle lane network; data mining; dockless bike sharing