Version 1
: Received: 21 February 2024 / Approved: 22 February 2024 / Online: 22 February 2024 (07:52:31 CET)
How to cite:
Jafarzadehfadaki, M.; Sisiopiku, V.P. Exploration of Shared E-Scooter Adoption Determinants and Latent Factors in Urban Settings Using Machine Learning. Preprints2024, 2024021264. https://doi.org/10.20944/preprints202402.1264.v1
Jafarzadehfadaki, M.; Sisiopiku, V.P. Exploration of Shared E-Scooter Adoption Determinants and Latent Factors in Urban Settings Using Machine Learning. Preprints 2024, 2024021264. https://doi.org/10.20944/preprints202402.1264.v1
Jafarzadehfadaki, M.; Sisiopiku, V.P. Exploration of Shared E-Scooter Adoption Determinants and Latent Factors in Urban Settings Using Machine Learning. Preprints2024, 2024021264. https://doi.org/10.20944/preprints202402.1264.v1
APA Style
Jafarzadehfadaki, M., & Sisiopiku, V.P. (2024). Exploration of Shared E-Scooter Adoption Determinants and Latent Factors in Urban Settings Using Machine Learning. Preprints. https://doi.org/10.20944/preprints202402.1264.v1
Chicago/Turabian Style
Jafarzadehfadaki, M. and Virginia P. Sisiopiku. 2024 "Exploration of Shared E-Scooter Adoption Determinants and Latent Factors in Urban Settings Using Machine Learning" Preprints. https://doi.org/10.20944/preprints202402.1264.v1
Abstract
E-scooters are a micromobility transportation option for completing short trips. In recent years, many cities welcomed shared e-scooters in an effort to offer more mode choices, increase travelers' convenience, and reduce automobile use in their service areas. However, a knowledge gap still remains regarding the acceptability of shared e-scooters as a transportation option, which motivates additional research on user and non-user preferences and attitudes toward e-scooter use. This study investigates latent variables impacting the adoption of shared e-scooters in urban areas, focusing on mode choice factors and attitudes towards e-scooter use and car use. The study utilizes machine learning (ML) techniques and SHAP analysis to analyze survey data (N=1196) collected from travelers in Washington, D.C., Miami, FL, and Los Angeles, CA, in 2021 and 2022. A comparative analysis is performed to develop comprehensive demographic profiles of e-scooter users. The analysis reveals gender (male), age (25-39 years age group), higher income, and educational background as the most relevant factors toward e-scooter use. Attitudinal variations among e-scooter users and non-users underscore the complexity of perceptions toward e-scooter use, with significant differences in mode choice factors and attitudes toward the use of e-scooters and private vehicles. Notably, educational background ranked as a significant factor in Washington, D.C., and Miami, while Factor 3, derived from factor analysis and encompassing car use attitudes and the utilization of technology, emerged as influential in Los Angeles. This research contributes fresh insights into factors shaping e-scooter adoption, offering a foundation for informed urban transportation planning and policymaking. The holistic approach showcased in this study enhances understanding of shared micromobility and its implications for urban mobility.
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
