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Journal article
Leveraging computer vision for predicting collision risks: a cross-sectional analysis of 2019-2021 fatal collisions in the USA
Injury prevention, ip-2023-045153
06 Jun 2024
PMID: 38844338
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Objective The USA has higher rates of fatal motor vehicle collisions than most high-income countries. Previous studies examining the role of the built environment were generally limited to small geographic areas or single cities. This study aims to quantify associations between built environment characteristics and traffic collisions in the USA.Methods Built environment characteristics were derived from Google Street View images and summarised at the census tract level. Fatal traffic collisions were obtained from the 2019-2021 Fatality Analysis Reporting System. Fatal and non-fatal traffic collisions in Washington DC were obtained from the District Department of Transportation. Adjusted Poisson regression models examined whether built environment characteristics are related to motor vehicle collisions in the USA, controlling for census tract sociodemographic characteristics.Results Census tracts in the highest tertile of sidewalks, single-lane roads, streetlights and street greenness had 70%, 50%, 30% and 26% fewer fatal vehicle collisions compared with those in the lowest tertile. Street greenness and single-lane roads were associated with 37% and 38% fewer pedestrian-involved and cyclist-involved fatal collisions. Analyses with fatal and non-fatal collisions in Washington DC found streetlights and stop signs were associated with fewer pedestrians and cyclists-involved vehicle collisions while road construction had an adverse association.Conclusion This study demonstrates the utility of using data algorithms that can automatically analyse street segments to create indicators of the built environment to enhance understanding of large-scale patterns and inform interventions to decrease road traffic injuries and fatalities.
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Details
- Title
- Leveraging computer vision for predicting collision risks: a cross-sectional analysis of 2019-2021 fatal collisions in the USA
- Creators
- Quynh C. Nguyen - University of Maryland, College ParkMitra Alirezaei - University of UtahXiaohe Yue - University of Maryland, College ParkHeran Mane - University of Maryland, College ParkDapeng Li - University of AlabamaLingjun Zhao - University of Maryland, College ParkThu T. Nguyen - University of Maryland, College ParkRithik Patel - BiostatWeijun Yu - University of Maryland, College ParkMing Hu - University of Notre DameD. Alex Quistberg - Drexel UniversityTolga Tasdizen - University of Utah
- Publication Details
- Injury prevention, ip-2023-045153
- Publisher
- Bmj Publishing Group
- Number of pages
- 6
- Grant note
- R01LM012849 / National Library of Medicine; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Library of Medicine (NLM) R01MD015716; R01MD016037 / National Institute on Minority Health and Health Disparities; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute on Minority Health & Health Disparities (NIMHD)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Urban Health Collaborative; Environmental and Occupational Health
- Web of Science ID
- WOS:001241836900001
- Scopus ID
- 2-s2.0-85196266855
- Other Identifier
- 991021889608404721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Public, Environmental & Occupational Health