Understanding and Predicting the Usage of Shared Electric Scooter Services on University Campuses

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 alter...
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Autor*in:	Seyed Mohammad Hossein Moosavi [verfasserIn] Zhenliang Ma [verfasserIn] Danial Jahed Armaghani [verfasserIn] Mahdi Aghaabbasi [verfasserIn] Mogana Darshini Ganggayah [verfasserIn] Yuen Choon Wah [verfasserIn] Dmitrii Vladimirovich Ulrikh [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

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

Übergeordnetes Werk:	In: Applied Sciences - MDPI AG, 2012, 12(2022), 18, p 9392
Übergeordnetes Werk:	volume:12 ; year:2022 ; number:18, p 9392

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/app12189392

Katalog-ID:	DOAJ02335447X

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callnumber-first	T - Technology
author	Seyed Mohammad Hossein Moosavi
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topic	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc green campus misc shared free-floating electric scooter misc usage frequency prediction misc decision tree misc random forest misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
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title	Understanding and Predicting the Usage of Shared Electric Scooter Services on University Campuses
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title_sort	understanding and predicting the usage of shared electric scooter services on university campuses
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title_auth	Understanding and Predicting the Usage of Shared Electric Scooter Services on University Campuses
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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Understanding and Predicting the Usage of Shared Electric Scooter Services on University Campuses
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Understanding and Predicting the Usage of Shared Electric Scooter Services on University Campuses

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