Song, Jie & Zhang, Liye & Qin, Zheng & Ramli, Muhamad Azfar. (2021). A spatiotemporal dynamic analyses approach for dockless bike-share system. Computers Environment and Urban Systems. 85. 101566. 10.1016/j.compenvurbsys.2020.101566.
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.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research / project is supported by the National Research Foundation, Prime Minister’s Office, Singapore - Urban Mobility Grant Challenge Program
Grant Reference no. : Award No. UMGC-L005
Description:
Please find the link to the article at the publisher's URL: https://doi.org/10.1016/j.compenvurbsys.2020.101566