Publication Type
Journal Article
Version
publishedVersion
Publication Date
4-2022
Abstract
Dockless e-scooter sharing, as a new shared micromobility service, has quickly gained popularity in recent years. In this paper, we present a practical approach to estimating e-scooter flow patterns without knowing the actual routes taken by the e-scooter riders. Our method takes advantage of a huge open dataset that contains the origins and destinations of millions of trips. We show that our models can help cities better support the emerging shared micromobility service. The additional information generated in the modeling process can also be useful for a more refined analysis of e-scooter trips.
Keywords
Shared micromobility, e-scooter, big data, shortest path, most direct path
Discipline
Transportation | Urban Studies
Research Areas
Integrative Research Areas
Publication
Journal of Urban Technology
Volume
29
Issue
2
First Page
139
Last Page
157
ISSN
1063-0732
Identifier
10.1080/10630732.2020.1843384
Publisher
Taylor and Francis Group
Citation
CHEN, Feng; JIAO, Junfeng; and WANG, Haofeng.
Estimating e-scooter traffic flow using big data to support planning for micromobility. (2022). Journal of Urban Technology. 29, (2), 139-157.
Available at: https://ink.library.smu.edu.sg/cis_research/506
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1080/10630732.2020.1843384