Citation

Yan X, He J, Wu G, Zhang C, Liu Z, Wang C. Anal. Meth. Accid. Res. 2021; 32: e100189.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.amar.2021.100189

PMID

unavailable

Abstract

Alcohol consumption has been acknowledged as a critical determinant concerning the occurrence of vehicle crashes and their resulting injury severities. To investigate the weekly transferability and temporal stability of the contributors determining different injury severity levels in alcohol-impaired driving crashes, this paper employs two groups of random thresholds random parameters hierarchical ordered probit models. Three injury-severity categories are determined as outcome variables: no injury, minor injury and severe injury, while multiple factors are investigated as explanatory variables including driver characteristics, vehicle characteristics, roadway characteristics, environmental characteristics, crash characteristics and temporal characteristics. The weekly transferability and temporal stability of the models are examined through three groups of likelihood ratio tests. Marginal effects are also adopted to analyze the weekly transferability and temporal stability of the explanatory variables. Overall, the findings exhibit weekly variations and temporal instability while some indicators are also observed to be of relative weekly transferability including speeding, aggressive driving, proceeding, motorcycle, speed limit between 45 and 55 mph, curve, driveway, overturned, hit-fixed-object, vehicle age (5-9 years old). Besides, curve and passenger car indicators in weekday models present relative temporal stability. This paper can provide insights into preventing alcohol-impaired driving crashes and can potentially facilitate the development of the corresponding crash injury mitigation policies. More studies could be conducted integrating the advanced data-driven methods into the statistical models to simultaneously achieve inference and prediction.

Language: en

Keywords

Alcohol-impaired driving; Hierarchical ordered probit model; Random thresholds random parameters; Temporal stability; Transferability