THE ESTIMATION, ENSEMBLING AND COMPARISON OF METHODOLOGICAL TECHNIQUES USED IN MOTOR VEHICLE CRASH SEVERITY RESEARCH.

This study compares the performance of longstanding methodological techniques of multinomial logit and ordinal probit models with more recent methods of decision tree and artificial neural network models, and combines individual models into ensembles to test if the amalgamation of the multiple methodologies enhances the classification accuracy of crash injury severity outcomes. The models are estimated using 2002 to 2012 crash data from the Missouri State Highway Patrol and the variables examined include driver characteristics, temporal factors, weather conditions, road characteristics, and injury severity levels. The accuracy and discriminatory power of explaining crash severity outcomes among all methods are compared using classification tables and Area Under the Receiver Operating Characteristic curve values. The Chi-square Automatic Interaction Detection decision tree model is found to have the greatest accuracy and discriminatory power relative to all evaluated approaches. [ABSTRACT FROM AUTHOR]