Toggle Main Menu Toggle Search

Open Access padlockePrints

Understanding spatial-temporal travel demand of private and shared e-bikes as a feeder mode of metro stations

Lookup NU author(s): Dr Shuo LiORCiD

Downloads


Licence

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2023 Elsevier LtdPrivate e-bikes and shared e-bikes are gradually becoming the preferred modes of feeders for a vast number of metro passengers in China, opening up new potential for sustainable urban transportation development. This study proposes a method to identify feeder behaviors from private and shared e-bikes to the metro using mobile phone signal data and shared e-bike operation order data in Nanning, China. Then, the Light Gradient Boosting Machine models are constructed to reveal the influence of various factors on the feeder demand for two kinds of e-bikes in metro stations, to promote integrated travel of e-bikes to the metro. The results show that the demand for private e-bikes in metro stations is significantly higher than that of shared e-bikes, and the demand for the two types of feeder modes during the morning and evening peak hours on weekdays is greater than that in other periods. Secondly, the density of educational facilities has a positive effect on the demand for both feeder modes, and it has a greater impact on the demand for private e-bikes. Thirdly, the distance to the city center has a non-linear effect on the demand for private e-bikes. The farther away from the city center, the more feeders use private e-bikes to travel, and the fewer feeders use shared e-bikes. These findings can help planners better understand how various factors influence feeder demand.


Publication metadata

Author(s): Liu S, Zhang F, Ji Y, Ma X, Liu Y, Li S, Zhou X

Publication type: Article

Publication status: Published

Journal: Journal of Cleaner Production

Year: 2023

Volume: 398

Print publication date: 10/04/2023

Online publication date: 27/02/2023

Acceptance date: 22/02/2023

Date deposited: 16/05/2023

ISSN (print): 0959-6526

Publisher: Elsevier Ltd

URL: https://doi.org/10.1016/j.jclepro.2023.136602

DOI: 10.1016/j.jclepro.2023.136602

ePrints DOI: 10.57711/vw7w-3451


Altmetrics

Altmetrics provided by Altmetric


Share