How do motorists’ pre-crash behaviors contribute to the injury severity of police officers? Using interpretable machine learning to untangle the behavioral pathways in crashes involving police vehicles

This study provides valuable insights into the behavioral pathway leading to police injury severities. By understanding the direct and indirect contributors to police injury severity, policymakers can develop targeted interventions to address these key risk factors and improve overall road safety for police officers.

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Institutions

Publication Details

Journal
Journal of Safety Research
Published
2025-06-30
DOI
https://doi.org/10.1016/j.jsr.2025.06.023
Citations
1
Primary Topic
Traffic and Road Safety
Type
article
Field-Weighted Citation Impact
1.30

Funders

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article

How do motorists’ pre-crash behaviors contribute to the injury severity of police officers? Using interpretable machine learning to untangle the behavioral pathways in crashes involving police vehicles

Ningzhe Xu, Steven Jones, Zihe Zhang, Jun Liu
1 citations
Journal of Safety Research
Traffic and Road Safety
1.30
article

How do motorists’ pre-crash behaviors contribute to the injury severity of police officers? Using interpretable machine learning to untangle the behavioral pathways in crashes involving police vehicles

Ningzhe Xu, Steven Jones, Zihe Zhang, Jun Liu
article en
1 citations

Abstract

This study provides valuable insights into the behavioral pathway leading to police injury severities. By understanding the direct and indirect contributors to police injury severity, policymakers can develop targeted interventions to address these key risk factors and improve overall road safety for police officers.

Journal of Safety ResearchVol. 94
University of Alabama (US), University Transportation Center for Alabama (US)
Centers for Disease Control and Prevention
Gender equality
Openalex Percentile: Top 20%
Traffic and Road Safety
1.30
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