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Journal Article

Citation

Xu N, Liu J, Zhang Z, Jones S. Safety Sci. 2024; 172: e106406.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.ssci.2023.106406

PMID

unavailable

Abstract

Traffic crashes are the leading cause of injuries and deaths for police officers in the US. There has been limited research to investigate the characteristics of traffic crashes that involve police officers. This study aims to investigate the relationships between different independent variables and police officer injury severity in traffic crashes. Considering the inherent spatial correlation between traffic crashes and the geographical environment, this study employs the ordered logit regression and the Geographically Weighted Ordered Logistic Regression (GWOLR) approach to identify the contributing factors of police officers' injuries in traffic crashes and to quantify the correlates of police injury severity in crashes. This study explored traffic crashes of five years (Number of observations = 4,231) that involved police officers in the State of Alabama. The results of the global ordered logit model illustrated that some independent variables, such as pre-crash behaviors, holiday, and road type, are statistically significantly related to police officer crash injury severity. Also, some variables, such as pre-crash behaviors and seatbelt usage, passed the non-stationarity tests. This suggests significant spatial variations in their relationships with the severity of injuries sustained by police officers in traffic crashes. The local correlates of police injury severity are valuable information for law enforcement agencies to localize strategies for improving officer safety on the road. For example, Move-over laws may need to be enhanced in the southeast Alabama area, where failing to yield (to police officers) seems to lead to increased traffic injury severities of police officers compared to other areas in Alabama.


Language: en

Keywords

Alabama; Police officers injury severity; Pre-crash behavior; Spatial modeling; Traffic crash

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