Modeling Injury Severity for Nighttime and Daytime Crashes by Using Random Parameter Logit Models Accounting for Heterogeneity in Means and Variances

Abstract

Understanding the factors contributing to crash severity, along with their influence degrees across different times of day, can assist in better highway design and in developing effective countermeasures for ameliorating highway safety (especially during nighttime). This study examines the influences of risk factors on crash severity, based on comparisons of nighttime and daytime crashes. By using a random parameter approach to account for unobserved heterogeneity, multivariate logit (RPML) models are proposed to analyze the crash severity based on the explanatory factors in terms of the crash, traffic, speed, road geometry, and sight characteristics. The goodness-of-fit and predictive measures highlight the better performance of the proposed models relative to standard models, as the proposed models reduce the unobserved heterogeneity and yield higher precision. In addition, the elasticity effects of the factors are calculated to investigate and compare their impact degrees in daytime and nighttime crashes. The findings could potentially be utilized to guide highway design and policies and to develop specific safety countermeasures.