An empirical analysis of dockless bike-sharing utilization and its explanatory factors: Case study from Shanghai, China
Artikel i vetenskaplig tidskrift, 2020

Revealing dockless bike-sharing utilization pattern and its explanatory factors are essential for urban planners and operators to improve the utilization and turnover of public bikes. This study explores the dockless bike-sharing utilization pattern from the perspective of bike using GPS-based bike origin-destination data collected in Shanghai, China. In this paper, utilization patterns are captured by decoupling several spatially cohesive regions with intensive bike use via non-negative matrix factorization. We then measure the utilization efficiency of bikes within each sub-region by calculating Time to booking (ToB) for each bike and explore how the built environment and social-demographic characteristics influence the bike-sharing utilization with ordinary least squares (OLS) regression and geographically weighted regression (GWR) models. The matrix factorization results indicate that the shared bikes mainly serve a certain area instead of the whole city. In addition, the GWR model shows higher explanatory power (Adjusted R2 = 0.774) than the OLS regression model (Adjusted R2 = 0.520), which suggests a close relationship between bike-sharing utilization and the selected explanatory variables. The coefficients of the GWR model reveal the spatial variations of the linkage between bike-sharing utilization and its explanatory factors across the study area. This study can shed light on understanding the demand and supply of shared bikes for rebalancing and provide support for operators to improve the dockless bike-sharing utilization efficiency.

Social-demographic characteristics

Empirical analysis

Dockless bike-sharing

GPS bike origin-destination data

GWR

Built environment

Författare

Aoyong Li

Eidgenössische Technische Hochschule Zürich (ETH)

Pengxiang Zhao

Eidgenössische Technische Hochschule Zürich (ETH)

Yizhe Huang

Shanghai Jiao Tong University

Kun Gao

Chalmers, Arkitektur och samhällsbyggnadsteknik, Geologi och geoteknik

Kay W. Axhausen

Eidgenössische Technische Hochschule Zürich (ETH)

Journal of Transport Geography

0966-6923 (ISSN)

Vol. 88 102828

Ämneskategorier

Annan data- och informationsvetenskap

Transportteknik och logistik

Ekonomisk geografi

DOI

10.1016/j.jtrangeo.2020.102828

Mer information

Senast uppdaterat

2020-09-14