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Understanding and Predicting the Usage of Shared Electric Scooter Services on University Campuses
Univ Malaya, Fac Engn, Dept Civil Engn, Kuala Lumpur 50603, Malaysia..ORCID iD: 0000-0002-3617-9896
KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.ORCID iD: 0000-0002-2141-0389
South Ural State Univ, Inst Architecture & Construct, Dept Urban Planning Engn Networks & Syst, 76 Lenin Prospect, Chelyabinsk 454080, Russia..
Chulalongkorn Univ, Transportat Inst, Bangkok 10330, Thailand..ORCID iD: 0000-0003-2874-5429
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2022 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 12, no 18, article id 9392Article in journal (Refereed) Published
Abstract [en]

Electric vehicles (EVs) have been progressing rapidly in urban transport systems given their potential in reducing emissions and energy consumptions. The Shared Free-Floating Electric Scooter (SFFES) is an emerging EV publicized to address the first-/last-mile problem in travel. It also offers alternatives for short-distance journeys using cars or ride-hailing services. However, very few SFFES studies have been carried out in developing countries and for university populations. Currently, many universities are facing an increased number of short-distance private car travels on campus. The study is designed to explore the attitudes and perceptions of students and staff towards SFFES usage on campus and the corresponding influencing factors. Three machine learning models were used to predict SFFES usage. Eleven important factors for using SFFESs on campus were identified via the supervised and unsupervised feature selection techniques, with the top three factors being daily travel mode, road features (e.g., green spaces) and age. The random forest model showed the highest accuracy in predicting the usage frequency of SFFESs (93.5%) using the selected 11 variables. A simulation-based optimization analysis was further conducted to discover the characterization of SFFES users, barriers/benefits of using SFFESs and safety concerns.

Place, publisher, year, edition, pages
MDPI AG , 2022. Vol. 12, no 18, article id 9392
Keywords [en]
green campus, shared free-floating electric scooter, usage frequency prediction, decision tree, random forest
National Category
Social Sciences Interdisciplinary Computer Sciences Information Systems
Identifiers
URN: urn:nbn:se:kth:diva-319542DOI: 10.3390/app12189392ISI: 000856214800001Scopus ID: 2-s2.0-85138650139OAI: oai:DiVA.org:kth-319542DiVA, id: diva2:1701191
Note

QC 20221005

Available from: 2022-10-05 Created: 2022-10-05 Last updated: 2022-10-05Bibliographically approved

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Ma, Zhenliang

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