The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Exploring the impact of shared autonomous vehicles on urban parking demand: An agent-based simulation approach
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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
Citations
Cites
Fagnant, D. J., & Kockelman, K. M. (2014) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2013.12.001 · Cited by 1,177
Ridesharing in North America: Past, Present, and Future
Chan, N. D., & Shaheen, S. (2011) · Transport Reviews · doi:10.1080/01441647.2011.621557 · Cited by 662
National Household Travel Survey
Garrett, M. (2014) · Encyclopedia of Transportation: Social Science and Policy · doi:10.4135/9781483346526.n341 · Cited by 479
Dynamic ride-sharing: A simulation study in metro Atlanta
Agatz, N. A. H., Erera, A. L., Savelsbergh, M. W. P., & Wang, X. (2011) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2011.05.017 · Cited by 476
Preparing a Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations
Fagnant, D. J., & Kockelman, K. M. (2014) · RePEc: Research Papers in Economics · Cited by 169
Martínez, L., Correia, G. H. D. A., & Viegas, J. M. (2014) · Journal of Advanced Transportation · doi:10.1002/atr.1283 · Cited by 150
Parking infrastructure: energy, emissions, and automobile life-cycle environmental accounting
Chester, M., Horvath, A., & Madanat, S. M. (2010) · Environmental Research Letters · doi:10.1088/1748-9326/5/3/034001 · Cited by 87
Dynamic Ride-Sharing and Optimal Fleet Sizing for a System of Shared Autonomous Vehicles
Fagnant, D. J., & Kockelman, K. M. (2015) · Transportation Research Board 94th Annual MeetingTransportation Research Board · Cited by 71
Transportation Energy Data Book: Edition 32
Davis, S. C., USDOE Office of Energy Efficiency and Renewable Energy (EERE), O. O. S. T. V. T. O. (., Diegel, S. W., & Boundy, R. G. (2013) · doi:10.2172/1110942 · Cited by 69
Hayes, B. (2011) · American Scientist · doi:10.1511/2011.92.362 · Cited by 57
(2015) · Transportation Research Board 94th Annual MeetingTransportation Research Board · Cited by 42
Travel demand policies for saving oil during a supply emergency
Noland, R. B., Cowart, W. A., & Fulton, L. M. (2005) · Energy Policy · doi:10.1016/j.enpol.2005.05.013 · Cited by 34
Patterson, E. M., & Mann, J. (2012) · Behavioral and Brain Sciences · doi:10.1017/s0140525x1100207x · Cited by 9
Flagged first, then by citations
Research works printed but not linked: 1
- Shoup, 2005, Vol. 206
Cited by
Sorted by citations
Milakis, D., van Arem, B., & van Wee, B. (2017) · Journal of Intelligent Transportation Systems · doi:10.1080/15472450.2017.1291351 · Cited by 963
Applications of artificial intelligence in transport: An overview
Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A. (2019) · Sustainability · doi:10.3390/su11010189 · Cited by 769
Shared autonomous vehicle services: A comprehensive review
Narayanan, S., Chaniotakis, E., & Antoniou, C. (2020) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2019.12.008 · Cited by 691
Faisal, A., Yigitcanlar, T., Kamruzzaman, M., & Currie, G. (2019) · Journal of Transport and Land Use · doi:10.5198/jtlu.2019.1405 · Cited by 427
“Different spatial distribution of urban parking demand will be evolved against different SAV operation strategies and client’s preferences (Zhang et al., 2015).”
BackgroundSoteropoulos, A., Berger, M., & Ciari, F. (2019) · Transport Reviews · doi:10.1080/01441647.2018.1523253 · Cited by 414
Metadata
- 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
- Social Sciences › Transportation › Transportation and Mobility Innovations
- Social Sciences › Transportation › 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
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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.
Statistics and arithmetic are recomputed, not judged.
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