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Cited by 450

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A Review of Evidence-Based Traffic Engineering Measures Designed to Reduce Pedestrian–Motor Vehicle Crashes

Open access
Retting, Richard A., Ferguson, Susan A., McCartt, Anne T.·2003·American Journal of Public Health

Abstract

We provide a brief critical review and assessment of engineering modifications to the built environment that can reduce the risk of pedestrian injuries. In our review, we used the Transportation Research Information Services database to conduct a search for studies on engineering countermeasures documented in the scientific literature. We classified countermeasures into 3 categories—speed control, separation of pedestrians from vehicles, and measures that increase the visibility and conspicuity of pedestrians. We determined the measures and settings with the greatest potential for crash prevention. Our review, which emphasized inclusion of studies with adequate methodological designs, showed that modification of the built environment can substantially reduce the risk of pedestrian–vehicle crashes.

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Metadata

Authors
Retting, R. A., Ferguson, S. A., & McCartt, A. T.
Venue
American Journal of Public Health
Published
2003-09
Volume
93
Issue
9
Pages
1456-1463
ISSN
  • 0090-0036 (ISSN-L)
  • 1541-0048
OpenAlex
W2095760087
Cited by
450
References
0
FWCI
23.67
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: A Review of Evidence-Based Traffic Engineering Measures Designed to Reduce Pedestrian–Motor Vehicle Crashes. https://app.cochat.ai/papers/10.2105/ajph.93.9.1456
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Retting, R. A., Ferguson, S. A., & McCartt, A. T. (2003). A Review of Evidence-Based Traffic Engineering Measures Designed to Reduce Pedestrian–Motor Vehicle Crashes. American Journal of Public Health. https://doi.org/10.2105/ajph.93.9.1456

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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.