Mixed logit approach to modeling the severity of pedestrian-injury in pedestrian-vehicle crashes in North Carolina: Accounting for unobserved heterogeneity

In transportation, pedestrians are among the most vulnerable entities. Each year, a total of about 2,000 pedestrians are reported to be involved in traffic crashes with vehicles in North Carolina. Research efforts are needed to identify influencing factors and develop safety improvement measures for pedestrians. This study applies mixed logit (ML) model approach to exploring the potential unobserved heterogeneities across individual injury observations. Factors that significantly contribute to pedestrian injury severities resulting from pedestrian-vehicle crashes are examined under a variety of categories, including motorist, pedestrian, environmental, and roadway (etc.) characteristics. Police reported pedestrian-vehicle crash data collected from 2007 to 2014 in North Carolina are utilized. Parameter estimates and associated elasticities are used to interpret the results.

Automated summary

This study utilizes a mixed logit model to analyze pedestrian-vehicle crash data from North Carolina between 2007 and 2014. The research aims to identify factors contributing to the severity of pedestrian injuries and propose safety improvement measures.