| dc.description.abstract | Crash severity outcomes are random and are influenced by the interactions of vehicle, driver, crash, road, and environmental factors. Limited research has attempted to assess the direct and latent influence of road factors like pavement and shoulder conditions, crash and vehicle factors, along with weather and driver factors on crash severity outcomes. This work attempts to develop crash severity prediction models considering the direct and indirect influence of road factors measured with pavement distress conditions, shoulder type and condition, crash factors measured with crash type and collision partners, human and weather factors measured with crash time (for visibility and traffic condition), season of crash occurrence, and driver age and gender. The work models crash severity outcomes using the commonly used Ordered Probit model, which recognizes the inherent ordered nature of crash severity outcomes, and also using Structured Equation Modeling (SEM), which not only considers the direct influences of the predictor variables but also their unobserved or latent influence. The Ordered Probit model was developed to assess discrete change probabilities for each factor for each severity level outcomes. The direct and indirect influence of individual factors was analyzed in detail using the calibrated SEM model. It could be observed that pavement distress condition, shoulder type and condition, crash type, and collision partners play an important role in the determination of the severity level outcomes of crashes. The overall model fit for Ordered Probit was not significant, but the SEM calibrated model was significant, indicating that the SEM model can be calibrated reasonably with smaller crash datasets. | |
