The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Default user image.

Pengxiang Zhao

Researcher

Default user image.

Modeling the choice of shared micro-mobility services using XGBoost machine learning

Author

  • Qilin Ren
  • Pengxiang Zhao
  • A Mansourian

Editor

  • P, van Oosterom
  • H. Ploeger
  • A. Mansourian
  • S. Scheider
  • R. Lemmens
  • B. van Loenen

Summary, in English

In recent years, shared micro-mobility services (e.g., bikes, e-bikes, and e-scooters) have been popularized at a rapid pace worldwide, which provide more choices for people’s short and medium-distance travel. Accurately modeling the choice of these shared micro-mobility services is important for their regulation and management. However, little attention has been paid to modeling their choice, especially with machine learning. In this paper, we explore the potential of the XGBoost model to model the three types of shared micro-mobility services, including docked bike, docked e-bike, and dockless e-scooter, in Zurich, Switzerland. The model achieves an accuracy of 72.6%. Moreover, the permutation feature importance is implemented to interpret the model prediction. It is found that trip duration, trip distance, and difference in elevation present higher feature importance in the prediction. The findings are beneficial for urban planners and operators to further improve the shared micro-mobility services toward sustainable urban mobility.

Department/s

  • Centre for Advanced Middle Eastern Studies (CMES)
  • MECW: The Middle East in the Contemporary World
  • Dept of Physical Geography and Ecosystem Science
  • Centre for Geographical Information Systems (GIS Centre)
  • BECC: Biodiversity and Ecosystem services in a Changing Climate

Publishing year

2023

Language

English

Publication/Series

AGILE GIScience Ser

Volume

4

Issue

4

Document type

Book chapter

Publisher

Copernicus GmbH

Topic

  • Geosciences, Multidisciplinary

Keywords

  • Shared micro-mobility
  • Machine learning
  • Vehicle availability data
  • Feature importance
  • Mode choice

Conference name

26th AGILE Conference on Geographic Information Science

Conference date

2023-06-13 - 2023-06-16

Conference place

Delft, Netherlands

Status

Published