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Modeling individuals’ willingness to share trips with strangers in an autonomous vehicle future
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“In case of ride-sharing, the results from Lavieri & Bhat (2019) show that the users are less sensitive to the presence of strangers for commute trips, when compared to leisure-activity trips.”
From Shared autonomous vehicle services: A comprehensive review (2020) · cites this paper as Lavieri & Bhat (2019)
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- Liu, 2018, An agent-based simulation model for shared autonomous taxi system, Asian Transp. Stud., 5, 1
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Metadata
- Authors
- Lavieri, P. S., & Bhat, C. R.
- Affiliations
- The University of Melbourne (AU)
- Hong Kong Polytechnic University (HK)
- Corresponding author
- Bhat, Chandra R.
- Venue
- Transportation Research Part A: Policy and Practice
- Publisher
- Elsevier BV
- Published
- 2019-06
- Volume
- 124
- Pages
- 242-261
- ISSN
- 0965-8564 (ISSN-L)
- OpenAlex
- W2925564714
- Cited by
- 278
- References
- 56
- Citations by year
- 2026: 22 · 2025: 34 · 2024: 53 · 2023: 47 · 2022: 44 · 2021: 45 · 2020: 25 · 2019: 8
- FWCI
- 13.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Transportation Research Part A Policy and Practice (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Transportation Research Board 98th Annual MeetingTransportation Research Board
- Funders
- U.S. Department of Transportation through the Data-Supported Transportation Operations and Planning
- Topics
- Social Sciences › Transportation › Transportation and Mobility Innovations
- Social Sciences › Transportation › Transportation Planning and Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Keywords
- willingness to share, ride-sharing, autonomous vehicles, travel time sensitivity, vehicle occupancy, travel behavior, integrated choice and latent variable model, commute trips, leisure travel, income effects
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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