Civil and Environmental Engineering
First Advisor
Dr. Aemal Khattak
Second Advisor
Dr. Ronald Faller
Third Advisor
Dr. Massoum Moussavi
Date of this Version
Spring 5-4-2023
Citation
Farooq, M. U. (2023). The effects of inaccurate and missing highway-rail grade crossing inventory data on crash and severity model estimation and prediction (Doctoral dissertation). University of Nebraska Lincoln.
Abstract
Highway-Rail Grade Crossings (HRGCs) present a significant safety risk to motorists, pedestrians, and train passengers as they are intersections where roads and railways intersect. Every year, HRGCs in the US experience a high number of crashes leading to injuries and fatalities. Estimations of crash and severity models for HRGCs provide insights into safety and mitigation of the risk posed by such incidents. The accuracy of these models plays a vital role in predicting future crashes at these crossings, enabling necessary safety measures to be taken proactively.
In the United States, most of these models rely on the Federal Railroad Administration's (FRA) HRGCs inventory database, which serves as the primary source of information for these models. However, errors or incomplete information in this database can significantly impact the accuracy of the estimated crash model parameters and subsequent crash predictions.
This study examined the potential differences in expected number of crashes and severity obtained from the Federal Railroad Administration's (FRA) 2020 Accident Prediction and Severity (APS) model when using two different input datasets for 560 HRGCs in Nebraska. The first dataset was the unaltered, original FRA HRGCs inventory dataset, while the second was a field-validated inventory dataset, specifically for those 560 HRGCs. The results showed statistically significant differences in the expected number of crashes and severity predictions using the two different input datasets. Furthermore, to understand how data inaccuracy impacts model estimation for crash frequency and severity prediction, two new zero-inflated negative binomial models for crash prediction and two ordered probit models for crash severity, were estimated based on the two datasets. The analysis revealed significant differences in estimated parameters’ coefficients values of the base and comparison models, and different crash-risk rankings were obtained based on the two datasets.
The results emphasize the importance of obtaining accurate and complete inventory data when developing HRGCs crash and severity models to improve their precision and enhance their ability to predict and prevent crashes.
Advisor: Aemal J. Khattak
Comments
A DISSERTATION Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Doctor of Philosophy, Major: Civil Engineering (Transportation Systems Engineering), Under the Supervision of Professor Aemal J. Khattak. Lincoln, Nebraska: May, 2023
Copyright © 2023 Muhammad Umer Farooq