Citation

Li Q, Zhang E, Luca D, Fuerst F. Transp. Res. D Trans. Environ. 2024; 130: e104179.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.trd.2024.104179

PMID

unavailable

Abstract

To facilitate the tailoring of dockless bike-sharing and electric bike (e-bike) sharing services and assist in formulating effective regulations, this study aims to unravel the spatio-temporal travel patterns specific to e-bike-sharing and bike-sharing systems, utilising interpretable machine learning methods and a large-scale trip-level dataset in Kunming, China. The results show that shared bikes and e-bikes exhibit overall similarities and subtle differences in many aspects, such as trip attributes and spatial distribution. Additionally, both shared bikes and shared e-bikes have three basic temporal patterns for commuting and recreational purposes. Regarding the differences, e-bike sharing networks are more dispersed and bigger, and bike sharing tends to form densely connected clusters of flow, exhibiting a local concentration of activity. Besides, the commuting activities within e-bike sharing systems exhibit two patterns: direct travel to the destination and integration with public transit. In contrast, shared bikes predominantly rely on public transit transfers for commuting purposes.

Keywords

Big data mining; Network structure; Shared micro-mobility; Spatio-temporal travel pattern; Trip purposes