Evidence map›Paper›PMID 31554231›Full record

ArticleInternational journal of environmental research and public health2019

Intersections and Non-Intersections: A Protocol for Identifying Pedestrian Crash Risk Locations in GIS.

Mingyu Kang, Anne Vernez Moudon, Haena Kim, Linda Ng Boyle

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Mingyu KangKorea Research Institute for Human Settlements (KRIHS), Sejong-si 30147, Korea. mgkang@krihs.re.kr.ORCID 0000-0003-1443-0135
Anne Vernez MoudonUrban Form Lab and Department of Urban Design and Planning, University of Washington, Seattle, WA 98195, USA. moudon@uw.edu.
Haena KimDepartment of Civil Engineering, University of Washington, Seattle, WA 98195, USA. haenakim@uw.edu.
Linda Ng BoyleDepartment of Industrial & Systems Engineering, University of Washington, Seattle, WA 98195, USA. linda@uw.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Intersection and non-intersection locations are commonly used as spatial units of analysis for modeling pedestrian crashes. While both location types have been previously studied, comparing results is difficult given the different data and methods used to identify crash-risk locations. In this study, a systematic and replicable protocol was developed in GIS (Geographic Information System) to create a consistent spatial unit of analysis for use in pedestrian crash modelling. Four publicly accessible datasets were used to identify unique intersection and non-intersection locations: Roadway intersection points, roadway lanes, legal speed limits, and pedestrian crash records. Two algorithms were developed and tested using five search radii (ranging from 20 to 100 m) to assess the protocol reliability. The algorithms, which were designed to identify crash-risk locations at intersection and non-intersection areas detected 87.2% of the pedestrian crash locations (r: 20 m). Agreement rates between algorithm results and the crash data were 94.1% for intersection and 98.0% for non-intersection locations, respectively. The buffer size of 20 m generally showed the highest performance in the analyses. The present protocol offered an efficient and reliable method to create spatial analysis units for pedestrian crash modeling. It provided researchers a cost-effective method to identify unique intersection and non-intersection locations. Additional search radii should be tested in future studies to refine the capture of crash-risk locations.

Indexed as

Accidents, TrafficModels, TheoreticalPedestriansAlgorithmsGeographic Information SystemsHumansalgorithmpedestrian safetyspatial autocorrelation

Identifiers

PMID31554231
PMCPMC6801818

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.