Identifying latent mode-use propensity segments in an all-AV era
- Authors
- Kim Sung Hoo; Circella Giovanni; Mokhtarian Patricia L.
- Issue Date
- Dec-2019
- Publisher
- Pergamon Press Ltd.
- Keywords
- Autonomous vehicles; Behavioral response; Factor analysis; Latent class cluster analysis; Mode-use propensity; Perceptions
- Citation
- Transportation Research Part A: Policy and Practice, v.130, pp.192 - 207
- Indexed
- SCIE
SSCI
SCOPUS
- Journal Title
- Transportation Research Part A: Policy and Practice
- Volume
- 130
- Start Page
- 192
- End Page
- 207
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/111101
- DOI
- 10.1016/j.tra.2019.09.015
- ISSN
- 0965-8564
- Abstract
- This study offers an early glimpse of how individuals perceive the advantages/disadvantages of AVs, their mode-use intentions, and potential market segments with respect to mode use, should AVs eventually become the only way to travel by car. To do so, we implemented a statewide survey of Georgia residents (N = 2890) and using that data, we applied factor analyses to two blocks of AV-related statements. The first block measured 12 perceptions of AVs, and yielded two psychological constructs: AV pros (advantages/ benefits) and AV overuse cons (negative outcomes specifically associated with the excessive use of AVs). The second block of statements measured respondents' inclinations between AV and non-AV options for 12 hypothetical transportation "needs", and factor analysis identified four mode-use propensity constructs: AV(-inclined) over walk/bike, AV over flight, zero-occupant AV over occupied AV, and AV over transit. The main goal of the paper was to segment the sample on the basis of these four mode-use propensities, to identify clusters with similar propensity profiles or response vectors. We applied latent class cluster analysis to do so, and identified seven potential market segments: some preferring AV options in general, others preferring non-AV options or having unique propensity patterns based on certain contexts (e.g. long distance travel and vehicle occupancy). In the model, socio-demographics, geography, attitudes, and perceptions of AVs help characterize those market segments, and this provides a basis for deeper interpretation and consideration of policy implications.
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