First Advisor

Tanmoy Bhowmik

Term of Graduation

Fall 2025

Date of Publication

12-19-2025

Document Type

Thesis

Degree Name

Master of Science (M.S.) in Civil & Environmental Engineering

Department

Civil and Environmental Engineering

Language

English

Subjects

Behavioral Crash, Crash severity, Hot Zones, Integrated Multivariate Model, Sequential Interdependencies

Physical Description

1 online resource (vii, 70 pages)

Abstract

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.

Rights

© 2025 Pabitra Kumar Roy

In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

Persistent Identifier

https://archives.pdx.edu/ds/psu/44403

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