A spatiotemporal dynamic analyses approach for dockless bike-share system

Abstract

The landscape of cycling activities from a dockless bike-share system is dynamic over space and time. Decoding usage patterns from bike-share trips have been a highly charged area in the literature. Therefore, this study aims at developing an analytical approach to understanding the trip demands of bike-share and model the spatiotemporal dynamics of cycling flows. Under the proposed framework, global and local Moran's I indexes measure the spatial autocorrelation of cycling trips in different traffic zones, and community detection extracts the network structure of bike traffic. The developed approach is subsequently applied to the dockless bike-share system in Singapore. It is found that the spatial distribution of the cycling trips shows significant clustering pattern. Specifically, the global Moran's indexes of weekdays are larger than that of the weekends in the same time span and, moreover, the global Moran's indexes of peak hours are smaller than the off-peak hours on the same day. Several hotspots with the top high local Moran's I values are detected, which keep relatively stable during different times of the day on both weekdays and weekends. We also found that there existed a stable community structure of bike-share trips. In contrast, the average sizes of the top 15 communities on a weekday were statistically higher than those on the weekend, at a significance level of 0.01. The proposed modeling framework provides practice insights to bike fleet management, cycling path design, and other urban and transportation planning practices.