Exploring the impact of shared autonomous vehicles on urban parking demand: An agent-based simulation approach
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Valet or auto-pilot parking features of AVs are expected to find cheap or free parking spaces after dropping off the passenger, saving travel time or cost because passengers do not need to cruise for parking, walk to the vehicle, or pay for costly parking.
“Valet or auto-pilot parking features of AVs are expected to find cheap or free parking spaces after dropping off the passenger. This in turn saves travel time or cost for commuters or passengers because the passengers do not require: (a) Cruising for a parking space; (b) Walking to the vehicle to pick up; (c) Paying for costly parking”
From Understanding autonomous vehicles: A systematic literature review on capability, impact, planning and policy (2019) · cites this paper as Zhang, Guhathakurta, Fang, & Zhang, 2015
Agent-based model results show that clients adopting SAV system in lieu of conventional private car can eliminate up to 90% of parking demand at a low market penetration rate of 2%.
“The results of an agent-based model show that the clients adopting SAV system in lieu of conventional private car can eliminate up to 90% of parking demand at a low market penetration rate of 2%”
From Understanding autonomous vehicles: A systematic literature review on capability, impact, planning and policy (2019) · cites this paper as Zhang et al., 2015
Different spatial distribution of urban parking demand will be evolved against different SAV operation strategies and client's preferences.
“Different spatial distribution of urban parking demand will be evolved against different SAV operation strategies and client's preferences”
From Understanding autonomous vehicles: A systematic literature review on capability, impact, planning and policy (2019) · cites this paper as Zhang et al., 2015
The reduction in parking space can reach up to 90%.
“Zhang et al. (2015) conclude that the reduction can reach up to 90%.”
From Shared autonomous vehicle services: A comprehensive review (2020) · cites this paper as Zhang et al., 2015
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- Authors
- Zhang, W., Guhathakurta, S., Fang, J., & Zhang, G.
- Affiliations
- Georgia Institute of Technology (US)
- Venue
- Sustainable Cities and Society
- Publisher
- Elsevier BV
- Published
- 2015-12
- Volume
- 19
- Pages
- 34-45
- ISSN
- 2210-6707 (ISSN-L)
- OpenAlex
- W1009846835
- Cited by
- 422
- References
- 13
- Citations by year
- 2026: 28 · 2025: 29 · 2024: 44 · 2023: 50 · 2022: 46 · 2021: 59 · 2020: 65 · 2019: 48 · 2018: 32 · 2017: 16 · 2016: 4 · 2015: 1
- FWCI
- 17.20
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Sustainable Cities and Society (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Transportation and Mobility Innovations
- Physical Sciences › Engineering › Building and Construction › Smart Parking Systems Research
- Social Sciences › Transportation › Transportation Planning and Optimization
- Keywords
- shared autonomous vehicles, urban parking demand, parking demand reduction, market penetration rate, urban form, agent-based modeling
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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