Summary: A growing concern related to large-truck crashes has increased in the State of Texas in recent years due to the potential economic impacts and level of injury severity that can be sustained. Yet, studies on large truck involved crashes highlighting the contributing factors leading to injury severity have not been conducted in detail in the State of Texas especially for its interstate system. In this study, we analyze the contributing factors related to injury severity by utilizing Texas crash data based on a discrete outcome based model which accounts for possible unobserved heterogeneity related to human, vehicle and road-environment. We estimate a random parameter logit model (i.e., mixed logit) to predict the likelihood of five standard injury severity scales commonly used in Crash Records Information System (CRIS) in Texas – fatal, incapacitating, non-incapacitating, possible, and no injury (property damage only). Estimation findings indicate that the level of injury severity outcomes is highly influenced by a number of complex interactions between factors and the effects of the some factors can vary across observations. The contributing factors include drivers’ demographics, traffic flow condition, roadway geometrics, land use and temporal characteristics, weather, and lighting conditions.
Bio: Salvador Hernandez, Ph.D. is an Assistant Professor in the School of Civil and Construction Engineering at Oregon State University (OSU). He earned his Ph.D. degree in Transportation Infrastructure Systems Engineering at Purdue University, West Lafayette, Indiana in 2010. His current areas of research and expertise include the application of econometric and statistical methods to variety of engineering problems, supply chain and logistics modeling, Intelligent Transportation Systems (ITS), and public and intermodal transportation. Prior to joining OSU he was an Assistant Professor at the University of Texas at El Paso (UTEP) where he successfully completed projects in the area of “Big Data.” Recent completed projects include the Synthesis of Operational Use of Real-Time Commercial Traffic Routing Data, Integration of Data Sources to Optimize Freight Transportation in Texas, Warehouse Location and Freight Attraction in the Greater El Paso Region, and El Paso Regional Ports of Entry Operations Plan. Prior to UTEP, he was a research assistant at Purdue University’s NEXTRAN center a USDOT Region V Regional University Transportation Center, where he conducted research on the viability of freight carrier collaboration through the use of demand modeling instruments (survey and econometric techniques) and network analysis and optimization tools. In addition, he has significant expertise in data multivariate techniques, which were applied to an exploratory analysis for freight carrier collaboration project at NEXTRANS. He teaches transportation systems, transportation safety, and logistics related undergraduate and graduate courses.