Exploring injury severity in head-on crashes using latent class clustering analysis and mixed logit model: A case study of North Carolina

•This paper examines injury severity of head-on crashes in North Carolina.•Latent class clustering analysis is conducted to reduce heterogeneity in the crash dataset.•Mixed logit models are developed to further capture unobserved heterogeneity within clusters.•Some variables are found to have random...

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Veröffentlicht in:	Accident analysis and prevention Jg. 135; S. 105388
Hauptverfasser:	Liu, Pengfei, Fan, Wei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England Elsevier Ltd 01.02.2020
Schlagworte:	Accidents, Traffic - mortality Adult Driving Under the Influence - statistics & numerical data Female Head-on crashes Humans Injury Severity Score Latent Class Analysis Latent class clustering Logistic Models Male Middle Aged Mixed logit model North Carolina - epidemiology Severity analysis United States Wounds and Injuries - epidemiology Young Adult North Carolina United States Severity analysis Mixed logit model Latent class clustering Head-on crashes
ISSN:	0001-4575, 1879-2057, 1879-2057
Online-Zugang:	Volltext
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