The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Residential Street Parking and Car Ownership: A Study of Households With Off-Street Parking in the New York City Region
Abstract
Problem, research strategy, and findings: Local governments’ minimum street-width standards may force developers to oversupply, and residents to pay excessively for, on-street parking in residential neighborhoods. Such oversupply is often presumed to both encourage car ownership and reduce housing affordability, although little useful evidence exists either way. This article examines the impact of street-parking supply on the car ownership of households with off-street parking in the New York City area. The off- and on-street parking supply for each household was measured through Google Street View and Bing Maps. The impact of on-street parking on car ownership levels was then estimated in an innovative multivariate model. The unique set-up of the case study ensures 1) the weak endogeneity between parking supply and car ownership and 2) the low correlation between off-street and on-street parking supply, two major methodological challenges of the study. Results show that free residential street parking increases private car ownership by nearly 9%; that is, the availability of free street parking explains 1 out of 11 cars owned by households with off-street parking. Takeaway for practice: These results offer support for community street standards that make on-street parking supply optional. They also suggest the merits of leaving the decisions of whether, and how many, on-street parking spaces to provide in new residential developments to private markets rather than regulation…
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“That is, all of the Ds have been found to be related to vehicle ownership in one study or another, like population and employment density (Bento et al., 2005; Chatman, 2013; Guo, 2013; Hess and Ong, 2002; Pinjari et al., 2011; Ryan and Han, 1999; Zegras, 2010), street network design (Bento et al., 2005; Bhat and Guo, 2007; Guo, 2013; Pinjari et al., 2011), land use diversity (Bento et al., 2005;”
From Key Enhancements to the WFRC/MAG Four-Step Travel Demand Model (2019) · cites this paper as Guo, 2013
In the citing paper
“A study in New York City shows that free residential street parking increases private car ownership by as much as 9% (Guo, 2013).”
From Key Enhancements to the WFRC/MAG Four-Step Travel Demand Model (2019) · cites this paper as Guo, 2013
Citations
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Cited by
Key Enhancements to the WFRC/MAG Four-Step Travel Demand Model
University of Utah, Ewing, R., Sabouri, S., University of Utah, Park, K., University of Utah, Lyons, T., University of Utah, Tian, G., & University of Utah (2019) · doi:10.15760/trec.246 · Cited by 3
Metadata
- Authors
- Guo, Z.
- Affiliations
- New York University (US)
- Corresponding author
- Guo, Zhan
- Venue
- Journal of the American Planning Association
- Publisher
- Informa UK Limited
- Published
- 2013-01-02
- Volume
- 79
- Issue
- 1
- Pages
- 32-48
- ISSN
- 0194-4363 (ISSN-L)
- 1939-0130
- OpenAlex
- W1930898307
- Cited by
- 80
- References
- 1
- Citations by year
- 2026: 7 · 2025: 5 · 2024: 5 · 2023: 8 · 2022: 4 · 2021: 9 · 2020: 11 · 2019: 4 · 2018: 5 · 2017: 5 · 2016: 5 · 2015: 1 · 2014: 8 · 2013: 2
- FWCI
- 11.72
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Planning Association (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Transportation › Transportation Planning and Optimization
- Social Sciences › Transportation › Smart Parking Systems Research
- Social Sciences › Transportation › Urban Transport and Accessibility
- Keywords
- car ownership, off-street parking, housing affordability, housing development, free parking, New York City
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
