Analysis of occupant injury severity in winter weather crashes: A fully Bayesian multivariate approach

• We developed fully Bayesian hierarchical multinomial logit models.
• We used a large sample of crash data for four winter seasons in Iowa.
• We nested occupants within crashes to capture the hierarchy in the crash data.
• Most of the occupant-level factors were found to significantly affect injury severity.
• Results revealed significant within-crash correlation in the study dataset.

Multivariate injury severity models that consider the cross-group heterogeneity in the crash data where individuals or occupants are nested within vehicles and vehicles are nested within crashes are limited in the literature. Most previous studies on crash injury severity were conducted at the crash level ignoring the potential correlation in severity for the vehicles involved in the same crashes or individuals involved in the same vehicles. Ignoring these correlation and dependence effects might result in underestimation of standard errors and erroneous inferences. The objective of this paper is to correctly determine the factors affecting occupant injury severity in winter seasons by addressing the within-crash and between-crash correlation of injury severity. To achieve this, fully Bayesian hierarchical multinomial logit models were developed for estimating occupant injury severity in weather-related crashes, non weather-related crashes, and all crashes. These models were developed using disaggregate crash data with occupants nested within crashes for four winter seasons in Iowa. Significant factors affecting occupant injury severity included factors related to occupants (gender, seating position, occupant trapped status, ejection status, and occupant protection used), as well as crash-level factors (road junction type, first harmful event and major cause of crash). Weather-related variables, such as visibility, pavement and air temperature, were also significant factors in winter weather crashes. Interaction effects involving crash-level variables and occupant-level variables were also found significant. Overall, the model diagnostics suggested significant within-crash correlation in the study dataset justifying the use of a multivariate model specification that addresses multivariate error term correlation issues.