Engineering

Case fatality rate

Transport engineering

Injury prevention

Statistics

Chromatography

Latent variable

Computer science

Environmental health

Homogeneous

Ordered logit

Poison control

Advanced driver assistance systems

Overtaking

Cluster (spacecraft)

Mathematics

Econometrics

Population

Artificial intelligence

Logit

Mixed logit

Combinatorics

Medicine

Chemistry

Programming language

Sample (material)

Crash

Latent class model

Logistic regression

Accident Analysis & Prevention

The single-vehicle crash has been recognized as a critical crash type due to its high fatality rate. In this study, a two-year crash dataset including all single-vehicle crashes in New Mexico is adopted to analyze the impact of contributing factors on driver injury severity. In order to capture the across-class heterogeneous effects, a latent class approach is designed to classify the whole dataset by maximizing the homogeneous effects within each cluster. The mixed logit model is subsequently developed on each cluster to account for the within-class unobserved heterogeneity and to further analyze the dataset. According to the estimation results, several variables including overturn, fixed object, and snowing, are found to be normally distributed in the observations in the overall sample, indicating there exist some heterogeneous effects in the dataset. Some fixed parameters, including rural, wet, overtaking, seatbelt used, 65 years old or older, etc., are also found to significantly influence driver injury severity. This study provides an insightful understanding of the impacts of these variables on driver injury severity in single-vehicle crashes, and a beneficial reference for developing effective countermeasures and strategies for mitigating driver injury severity.

Using latent class analysis and mixed logit model to explore risk factors on driver injury severity in single-vehicle crashes