Residual Control Chart for Binary Response with Multicollinearity Covariates by Neural Network Model

Quality control studies have dealt with symmetrical data having the same shape with respect to left and right. In this research, we propose the residual (r) control chart for binary asymmetrical (non-symmetric) data with multicollinearity between input variables via combining principal component analysis (PCA), functional PCA (FPCA) and the generalized linear model with probit and logit link functions, and neural network regression model. The motivation in this research is that the proposed control chart method can deal with both high-dimensional correlated multivariate data and high frequency functional multivariate data by neural network model and FPCA. We show that the neural network r control chart is relatively efficient to monitor the simulated and real binary response data with the narrow length of control limits

The Effects of Inaccurate and Missing Highway-Rail Grade Crossing Inventory Data on Crash and Severity Model Estimation and Prediction

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\u27s (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\u27s (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. Khatta