Modeling the choice of shared micro-mobility services using XGBoost machine learning
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3d069808-bafa-4945-8b3b-2da3bf082f67
- author
- Ren, Qilin ; Zhao, Pengxiang LU and Mansourian, A LU
- organization
- publishing date
- 2023
- 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
- 2024-05-30 08:36:44
@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}}, }