4.5 Article

Survey Data Analysis on Intention to Use Shared Mobility Services

Journal

JOURNAL OF ADVANCED TRANSPORTATION
Volume 2021, Issue -, Pages -

Publisher

WILEY-HINDAWI
DOI: 10.1155/2021/5585542

Keywords

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Funding

  1. National Research Foundation of Korea (NRF) grant - Ministry of Science, ICT and Future Planning [2020R1C1C1003296, N01200545]
  2. KAIST-KU joint Research Center, KAIST, South Korea
  3. National Research Foundation of Korea [2020R1C1C1003296] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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This study analyzed the factors affecting the intention to use shared mobility services, revealing significant effects of socio-demographic characteristics such as gender, car ownership, and education, as well as personal experience on the willingness to use shared mobility. These findings are expected to provide valuable insights for future planning of smart mobility services.
Shared mobility is a service that allows users to share various transportation modes and use them with reservations when necessary. It started with private automotive car-sharing and ride-sharing services. Currently, it operates on a wider range, including personal mobility devices such as electric bicycles and scooters. The purpose of this study is to derive a direction for providing future shared mobility services through analysis of factors affecting the usage intention of both current and prospective users. The survey targets 753 citizens living in Gyeonggi Province, Korea. The survey period is from February 12, 2020, to February 26, 2020. In this study, a logistic regression analysis is conducted to investigate the factors affecting the use intention of shared mobility. The analysis results show that gender, car ownership, and education, among variables reflecting socio-demographic characteristics, have significant effects on intention to use shared mobility. Moreover, we find that experience factors, including mainly used transportation modes, ownership of shared mobility device, past experience in similar services, satisfaction of existing shared mobility services, and distance from the home to the nearest bus stop, are also statistically influential. The analysis results are expected to lay the foundation for the introduction of shared mobility services and can be used as data for planning smart mobility services in the future.

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