This study resulted in a model-averaging methodology that predicts crash injury risk using vehicle, demographic, and morphomic variables and assesses the importance of individual predictors. The effectiveness of this methodology was illustrated through analysis of occupant chest injuries in frontal vehicle crashes. The crash data were obtained from the International Center for Automotive Medici...

This study adopted the Highway Safety Information System’s (HSIS) data for crashes occurred on road segments to develop supervised machine learning prediction models. Five models are developed: Linear Regression (LR), Generalize Additive Model (GAM), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). A comparison among five model was performed using root mean...

subsea structures such as manifolds, line pipes and flow lines are important investments. also because of the sensitivity of environmental issues, corrosion of these structures is of vital importance. subsea corrosion management is different from on-shore and shallow water off-shore corrosion management in mainly three factors: materials, corrosion management practice and cathodic protection. t...

Single vehicle crashes contribute to a significant amount of fatalities in the United States. At the same time, fatality crash involvement rates of young and older drivers are well above the average and both groups are identified as critical groups when it comes to highway safety. Therefore, the study described in this paper developed separate models to predict crash severity of single vehicle ...

Accurate real-time crash risk evaluation is essential for making prevention strategy in order to proactively improve traffic safety. Quite a number of models have been developed to evaluate traffic crash risk, by using real-time surveillance data. In this paper, the basic idea of traffic safety region is introduced into highway crash risk evaluation. Traffic safety region aims to describe the s...

In crash frequency models, frequency by severity level are examined using multivariate count models. In these multivariate approaches the impact of exogenous variables is quantified through the propensity component of count models. The main interaction among variables across different severity levels is sought through unobserved effects i.e. there is no interaction of observed effects across th...