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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Vydáno v:Accident analysis and prevention Ročník 135; s. 105388
Hlavní autoři: Liu, Pengfei, Fan, Wei
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Elsevier Ltd 01.02.2020
Témata:
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
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Shrnutí:•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 effects across observations in specific clusters.•Relevant countermeasures are developed and future research directions are discussed. Although only 2 % of crashes are head-on crashes in the United States, they account for over 10 % of all crash-related fatalities. This study aims to investigate the contributing factors that affect the injury severity of head-on crashes and develop appropriate countermeasures. Due to the unobserved heterogeneity inherent in the crash data, a latent class clustering analysis is firstly conducted to segment the head-on crashes into relatively homogeneous clusters. Then, mixed logit models are developed to further explore the unobserved heterogeneity within each cluster. Analyses are performed based on the data collected from the Highway Safety Information System (HSIS) from 2005 to 2013 in North Carolina. The estimated parameters and associated marginal effects are combined to interpret significant variables of the developed models. The proposed method is able to uncover the heterogeneity within the whole dataset and the homogeneous clusters. Results of this research can provide more reliable and insightful information to engineers and policy makers regarding the contributing factors to head-on crashes.
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ISSN:0001-4575
1879-2057
1879-2057
DOI:10.1016/j.aap.2019.105388