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- Zhao, PengxiangLund University,Lunds universitet,Centrum för geografiska informationssystem (GIS-centrum),Institutionen för naturgeografi och ekosystemvetenskap,Naturvetenskapliga fakulteten,Centrum för Mellanösternstudier (CMES),Samhällsvetenskapliga institutioner och centrumbildningar,Samhällsvetenskapliga fakulteten,Centre for Geographical Information Systems (GIS Centre),Dept of Physical Geography and Ecosystem Science,Faculty of Science,Centre for Advanced Middle Eastern Studies (CMES),Departments of Administrative, Economic and Social Sciences,Faculty of Social Sciences (author)
A machine learning based approach for predicting usage efficiency of shared e-scooters using vehicle availability data
- Article/chapterEnglish2022
Publisher, publication year, extent ...
- 2022-06-10
- Copernicus GmbH,2022
Numbers
- LIBRIS-ID:oai:lup.lub.lu.se:68f593e3-6bdc-4f62-9268-6da0e70c8606
- https://lup.lub.lu.se/record/68f593e3-6bdc-4f62-9268-6da0e70c8606URI
- https://doi.org/10.5194/agile-giss-3-20-2022DOI
Supplementary language notes
- Language:English
- Summary in:English
Part of subdatabase
- SwePubSwePub
Classification
Notes
- 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.
Subject headings and genre
- NATURVETENSKAP Geovetenskap och miljövetenskap hsv//swe
- NATURAL SCIENCES Earth and Related Environmental Sciences hsv//eng
- Micro-mobility
- E-scootersharing
- Usage efficiency
- Spatiotemporalanalysis
- Machine learning
- Vehicle availability data
- Artificial Intelligence (AI)
- Geospatial Artificial Intelligence (GeoAI)
Added entries (persons, corporate bodies, meetings, titles ...)
- Li, AoyongTsinghua University (author)
- Mansourian, ALund University,Lunds universitet,Centrum för geografiska informationssystem (GIS-centrum),Institutionen för naturgeografi och ekosystemvetenskap,Naturvetenskapliga fakulteten,Centrum för Mellanösternstudier (CMES),Samhällsvetenskapliga institutioner och centrumbildningar,Samhällsvetenskapliga fakulteten,Centre for Geographical Information Systems (GIS Centre),Dept of Physical Geography and Ecosystem Science,Faculty of Science,Centre for Advanced Middle Eastern Studies (CMES),Departments of Administrative, Economic and Social Sciences,Faculty of Social Sciences(Swepub:lu)nate-amo (author)
- Parseliunas, E. (editor)
- Mansourian, A. (editor)
- Partsinevelos, P. (editor)
- Suziedelyte-Visockiene, J. (editor)
- Centrum för geografiska informationssystem (GIS-centrum)Institutionen för naturgeografi och ekosystemvetenskap (creator_code:org_t)
Related titles
- In:Artificial Intelligence in the service of Geospatial Technologies: Copernicus GmbH3
- In:AGILE: GIScience Series: Copernicus GmbH32700-8150
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