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Cited by 344

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Urban Form and Pedestrian Choices: Study of Austin Neighborhoods

Handy, Susan L.·1996·Transportation Research Record: Journal of the Transportation Research Board

Abstract

Supporters of the New Urbanism suggest that the right design will encourage walking, thereby encouraging interaction and a greater sense of community and discouraging automobile dependence. Existing research provides insufficient evidence to support this belief, however, largely because of limitations in the data and methodologies that researchers have used. The research described moves beyond a simple test of correlations to an exploration of how urban form fits into a more comprehensive model of choices about pedestrian trips. First, a model for individual choices about pedestrian trips is proposed. Second, the results of a study of six neighborhoods in Austin, Texas, are presented. Data from a survey of residents in these neighborhoods support the proposed model and suggest that certain aspects of urban form can play an important role in encouraging walks to a destination but that the savings in travel from the substitution of walking for driving is likely to be small.

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Metadata

Authors
Handy, S. L.
Venue
Transportation Research Record: Journal of the Transportation Research Board
Published
1996-01
Volume
1552
Issue
1
Pages
135-144
ISSN
  • 0361-1981 (ISSN-L)
  • 2169-4052
OpenAlex
W2056010747
Cited by
344
References
4
FWCI
15.73
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Urban Form and Pedestrian Choices: Study of Austin Neighborhoods. https://app.cochat.ai/papers/10.1177/0361198196155200119
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Handy, S. L. (1996). Urban Form and Pedestrian Choices: Study of Austin Neighborhoods. Transportation Research Record: Journal of the Transportation Research Board. https://doi.org/10.1177/0361198196155200119

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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.