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Pengxiang Zhao

Forskare

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

Författare

  • Qilin Ren
  • Pengxiang Zhao
  • A Mansourian

Redaktör

  • 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.

Avdelning/ar

  • Centrum för Mellanösternstudier (CMES)
  • MECW: The Middle East in the Contemporary World
  • Institutionen för naturgeografi och ekosystemvetenskap
  • Centrum för geografiska informationssystem (GIS-centrum)
  • BECC: Biodiversity and Ecosystem services in a Changing Climate

Publiceringsår

2023

Språk

Engelska

Publikation/Tidskrift/Serie

AGILE GIScience Ser

Volym

4

Issue

4

Dokumenttyp

Del av eller Kapitel i bok

Förlag

Copernicus GmbH

Ämne

  • Geosciences, Multidisciplinary

Nyckelord

  • 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