Understanding and predicting the usage of shared electric scooter services on university campuses

Seyed Mohammad Hossein Moosavi, Zhenliang Ma, Danial Jahed Armaghani, Mahdi Aghaabbasi, Mogana Darshini Ganggayah, Yuen Choon Wah, Dmitrii Vladimirovich Ulrikh

Research output: Contribution to journalArticleResearchpeer-review

8 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number9392
Number of pages28
JournalApplied Sciences
Volume12
Issue number18
DOIs
Publication statusPublished - 2 Sept 2022

Keywords

  • decision tree
  • green campus
  • random forest
  • shared free-floating electric scooter
  • usage frequency prediction

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