Analysis of residents’ stated preferences of shared micro-mobility devices using regression-text mining approach
Prior to establishing micro-mobility schemes, operators gather residents’ willingness to use them. However, an inadequate survey setting may lead to demand over- or under-estimation. This study used survey data from Gilbert City, Arizona, to understand the implications of the stated preferences of micro-mobility devices. The application of multinomial logit regression and text networks revealed a great disparity between the stated ‘want’ and ‘use’ of micro-mobility devices. Male residents were more likely to respond that they wanted and would use electric scooters. Conversely, older residents were less likely to respond that they wanted and would use either electric scooters or dockless bikes. High-income residents were more likely to want either electric scooters or docked bike-sharing in the city, but do not plan to use them. Additionally, residents’ comments focused more on electric scooters than other schemes. The implications of the findings to operators, engineers, and planners are discussed in the study.
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/1767712
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Supplemental Notes:
- © 2022 Informa UK Limited, trading as Taylor & Francis Group. Abstract reprinted with permission of Taylor & Francis.
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Authors:
- Kutela, Boniphace
- Novat, Norris
- Adanu, Emmanuel Kofi
- Kidando, Emmanuel
- Langa, Neema
- Publication Date: 2022-2
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: pp 159-178
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Serial:
- Transportation Planning and Technology
- Volume: 45
- Issue Number: 2
- Publisher: Taylor & Francis
- ISSN: 0308-1060
- Serial URL: https://www.tandfonline.com/toc/gtpt20/current
Subject/Index Terms
- TRT Terms: Attitudes; Consumer preferences; Income; Micromobility
- Geographic Terms: Gilbert (Arizona)
- Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting;
Filing Info
- Accession Number: 01853515
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 29 2022 4:55PM