Abstract

Crash severity models are typically developed using police reported injury severity databases. However, several research studies have identified various challenges associated with police reported data. Therefore, the current dissertation is focusing on developing high resolution crash severity models based on medical professional driver injury severity reported using Abbreviated Injury Scale for eight body regions. The dissertation focused on developing a disaggregate injury severity modeling framework that can enhance the estimation accuracy of independent variable impacts on severity. Within this broad research vision, the dissertation has multiple objectives. First, a joint random parameters multivariate model structure with as many dimensions as severity by body location was developed. The empirical analysis involves the estimation of Random Parameters Multivariate Generalized Ordered Probit Model that allows for the influence of common unobserved factors affecting the vehicle occupant severity across body locations. Second, we incorporate the influence of temporal factors (observed and unobserved) within a multivariate model system for medical professional generated body region specific injury severity score. For this purpose, we adopt a hybrid econometric modeling approach that accommodates for the unobserved factors. Third, the dissertation compares the predictive performance of the state-of-the-art econometric model with the predictive performance of state-of-the-art machine learning methods. We consider machine learning approaches such as Random Forest, Logistic Regression, Boosting, and Support Vector Machine. Finally, the dissertation applied two approaches. First, analyze dependent variables with an ordered logit framework using the six injury severity levels. The second approach is adopting a hurdle ordered logit framework by splitting the dependent variable into two stages: binary and truncated which exclude the zero cases. The model performance of these approaches are compared using the data of two body regions.

Notes

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Graduation Date

2022

Semester

Spring

Advisor

Eluru, Naveen

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Civil, Environmental and Construction Engineering

Degree Program

Civil Engineering

Format

application/pdf

Identifier

CFE0009446; DP0027169

URL

https://purls.library.ucf.edu/go/DP0027169

Language

English

Release Date

November 2023

Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Open Access)

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