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Pengxiang Zhao

Researcher

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A machine learning based approach for predicting usage efficiency of shared e-scooters using vehicle availability data

Author

  • Pengxiang Zhao
  • Aoyong Li
  • A Mansourian

Editor

  • E. Parseliunas
  • A. Mansourian
  • P. Partsinevelos
  • J. Suziedelyte-Visockiene

Summary, in English

Shared electric scooters (e-scooters) have been rapidly growing in popularity across Europe over the past three years, which can bring various environmental and socioeconomic benefits. However, how to further improve the usage efficiency of shared e-scooters is still a major concern for micro-mobility operators and city planners. This paper proposes a machine learning based approach to predict the usage efficiency of shared e-scooters using GPS-based vehicle availability data. First, the usage efficiency of shared e-scooters is measured with the indicator Time to Booking at the trip level. Second, ten exploratory variables in time and space are calculated as features for the prediction based on the e-scooter trips and other related data. Last, three typical machine learning methods, including logistical regression, artificial neural network and random forest are applied to predict the usage efficiency by inputting the features. Besides, the variable importance is evaluated by taking the random forest model as an example. The results show that the random forest model yields the best prediction performance (accuracy = 71.2%, F1 = 78.0%), and the variables like the hour of day and POI density present high variable importance. The findings of this study will be beneficial for micro-mobility operators and city planners to design policies and strategies for further improving the usage efficiency of e-scooter sharing services.

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

2022

Language

English

Publication/Series

AGILE: GIScience Series, 3, 20, 2022

Volume

3

Document type

Conference paper

Publisher

Copernicus GmbH

Topic

  • Earth and Related Environmental Sciences

Keywords

  • Micro-mobility
  • E-scootersharing
  • Usage efficiency
  • Spatiotemporalanalysis
  • Machine learning
  • Vehicle availability data
  • Artificial Intelligence (AI)
  • Geospatial Artificial Intelligence (GeoAI)

Conference name

25th AGILE Conference on Geographic Information Science

Conference date

2022-06-14 - 2022-06-17

Conference place

Vilnius, Lithuania

Status

Published