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Cited by 177

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Walk-and-Ride: Factors Influencing Pedestrian Access to Transit

Open access
Univerity of Califoria, Cervero, Robert·2001·Journal of Public Transportation

Abstract

The predominant means of reaching suburban rail stations in the United States is by private car. Transit villages strive, among other things, to convert larger shares of rail access trips to walk-and-ride, bike-and-ride, and bus-and-ride. Empirical evidence on how built environments influence walk-access to rail transit remains sketchy. In this article, analyses are carried out at two resolutions to address this question. Aggregate data from the San Francisco Bay Area reveal compact, mixed-use settings with minimal obstructions are conducive to walk-and-ride rail patronage. A disaggregate-level analysis of access trips to Washington Metrorail services by residents of Montgomery County, Maryland, shows that urban design, and particularly sidewalk provisions and street dimensions, significantly influence whether someone reaches a rail stop by foot or not. Elasticities are presented that summarize findings. The article concludes that conversion of park-and-ride lots to transit-oriented developments holds considerable promise for promoting walk-and-ride transit usage in years to come.

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Metadata

Authors
Califoria, U. O., & Cervero, R.
Venue
Journal of Public Transportation
Published
2001-09
Volume
3
Issue
4
Pages
1-23
ISSN
  • 1077-291X (ISSN-L)
  • 2375-0901
OpenAlex
W827342609
Cited by
177
References
0
FWCI
6.72
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Walk-and-Ride: Factors Influencing Pedestrian Access to Transit. https://app.cochat.ai/papers/10.5038/2375-0901.3.4.1
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Califoria, U. O., & Cervero, R. (2001). Walk-and-Ride: Factors Influencing Pedestrian Access to Transit. Journal of Public Transportation. https://doi.org/10.5038/2375-0901.3.4.1

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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.