A novel integrated approach to modeling and predicting crash frequency by crash event state

In this study, we propose a novel integrated parametric framework for analyzing multivariate crash count data based on linking a univariate count model for the total count of motor vehicle crashes across all possible crash states with a discrete choice model for crash event state given a crash. In doing so, we are able to use information at the disaggregate crash-level from an unordered model structure in analyzing the aggregate level crash count. To our knowledge, this is the first such model proposed in the econometric literature. We apply this approach in a demonstration exercise to examine the number of motor vehicle crashes in Census Block Groups (CBGs) in Austin, Texas, considering four injury severity levels. At the disaggregate level, we incorporate several explanatory variables such as the characteristics of the most severely injured individual and at-fault vehicle’s parties, crash time variables (time of day, weather), crash location variables, and CBG level variables. At the aggregate level, we consider CBG level variables, including road design factors, land-use variables, crash exposure factors, aggregate sociodemographic attributes, and crime and traffic violations related measures. Importantly, our results indicate a significant and positive linkage between the disaggregate crash event state dimensions and the total crash count. Through the use of elasticity measures, our results also clearly highlight the improved policy sensitivity of the integrated model framework.