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

Ren, Qilin ; Zhao, Pengxiang LU and Mansourian, A LU (2023) 26th AGILE Conference on Geographic Information Science In AGILE GIScience Ser 4(4).
Abstract
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... (More)
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. (Less)
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author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Shared micro-mobility, Machine learning, Vehicle availability data, Feature importance, Mode choice
host publication
26th AGILE Conference on Geographic Information Science : Spatial data for design - Spatial data for design
series title
AGILE GIScience Ser
editor
van Oosterom, P, ; Ploeger, H. ; Mansourian, A. ; Scheider, S. ; Lemmens, R. and van Loenen, B.
volume
4
issue
4
publisher
Copernicus GmbH
conference name
26th AGILE Conference on Geographic Information Science
conference location
Delft, Netherlands
conference dates
2023-06-13 - 2023-06-16
DOI
10.5194/agile-giss-4-39-2023
language
English
LU publication?
yes
id
3d069808-bafa-4945-8b3b-2da3bf082f67
date added to LUP
2023-09-01 13:12:59
date last changed
2024-02-14 15:08:24
@inbook{3d069808-bafa-4945-8b3b-2da3bf082f67,
  abstract     = {{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.}},
  author       = {{Ren, Qilin and Zhao, Pengxiang and Mansourian, A}},
  booktitle    = {{26th AGILE Conference on Geographic Information Science : Spatial data for design}},
  editor       = {{van Oosterom, P, and Ploeger, H. and Mansourian, A. and Scheider, S. and Lemmens, R. and van Loenen, B.}},
  keywords     = {{Shared micro-mobility; Machine learning; Vehicle availability data; Feature importance; Mode choice}},
  language     = {{eng}},
  number       = {{4}},
  publisher    = {{Copernicus GmbH}},
  series       = {{AGILE GIScience Ser}},
  title        = {{Modeling the choice of shared micro-mobility services using XGBoost machine learning}},
  url          = {{http://dx.doi.org/10.5194/agile-giss-4-39-2023}},
  doi          = {{10.5194/agile-giss-4-39-2023}},
  volume       = {{4}},
  year         = {{2023}},
}