Exploring Driver Injury Severity Using Latent Class Ordered Probit Model: A Case Study of Turkey

Abstract

There have been limited efforts to study the severity of traffic crashes in Turkey. This study is probably the first attempt to investigate the factors that contribute to driver injury severity patterns. Fatal injury crash data from 2015 to 2017 for the city of Mersin was used. A two-step approach was employed. First, latent class clustering was performed to capture unobserved heterogeneity inherent in the crash data. The crash database was separated into four clusters by maximizing the homogeneity within each cluster. Then, an ordered probit model was developed on each cluster to explore the factors that significantly affect the driver injury outcomes. Marginal effects were calculated to interpret the influence of significant variables across the injury levels in more detail. The presence of motorcycles, fixed objects, and run-off-road crashes were found to be the main factors associated with injury and fatality in all clusters. The results underlined the association between driving behavior and injury severity of drivers. Alcohol-impaired driving, speeding, and traffic sign/signal violations increase the likelihood of severe injury.