Modeling Large-Truck Involved Crashes: An Econometric Modeling Framework

This research aims to analyze large-truck involved crashes (i.e., having a gross vehicle weight rating more than 10,000 pounds) through the application of advanced econometric modeling techniques--namely, random parameter models (i.e., tobit regression, mixed logit). To achieve this, various national and state specific data sets are analyzed with the goal to provide an improved understanding of the complex interactions of contributing factors (e.g., factors related to drivers and occupants, vehicle, and road-environment) influencing large-truck crashes. Additionally, the modeling techniques considered in this research account for possible unobserved effects related to the data. The aforementioned econometric techniques provide an analytical foundation for exploring the contributing factors leading to large-truck involved crashes