An Integrated Multivariate Econometric Modeling Framework for Risky Driving Behavior Related Crashes: Evaluating Crash Risk and Severity Across Zones

Rapid advancements in crash modeling have yet to fully integrate multiple behavior-driven crash types and their severity outcomes within a single, scalable framework, specially in a way that captures the sequential nature of these behaviors where one risky action may amplify another. This study proposed an integrated multivariate econometric framework to jointly model crash frequency and severity outcomes for three major behaviorally driven crash types: alcohol-related, distraction-related, and aggressive-driving-related crashes. Specifically, using Oregon's 2022 census block group-level crash data, we propose an Integrated Multivariate Negative Binomial – Generalized Ordered Probit Fractional Split (IMNB–GOPFS) model to analyze these dimensions simultaneously while accounting for the sequential nature of the behavioral crash types. A comparison exercise in terms of model fit and predictive performance reveals the superior performance of the proposed framework over traditional non-integrated approaches, thus highlighting the existence and importance of capturing such interdependencies among behaviorally driven crashes. A brief elasticity assessment further highlights how several variables influence crash outcomes through both direct and behaviorally mediated impacts. Further, we apply the model results to identify high-risk zones and find that census block groups with increased truck exposure, greater rural and freeway road coverage, and larger variations in speed limits are consistently associated with elevated crash occurrences across multiple behavioral crash types. By focusing on the full spectrum of behavior-driven crash types and their severity outcomes, this study offers a comprehensive framework to support data-driven safety planning and proactive crash severity risk assessment at the planning level.