Driver Characteristics and Behaviors Associated with Higher Injury Severity Large Logging Truck Crashes on Public Roadways in Louisiana (2010-2018)

This dissertation involved an in-depth analysis of statewide crash data (2010-2018) from the Louisiana Department of Transportation and Development (LaDOTD) Highway Safety that focused on large logging truck crashes. The objective of this cross-sectional study was to determine driver characteristics and behaviors associated with more severe large logging truck crashes in Louisiana. The findings can be used to develop targeted educational activities to promote roadway safety and reduce the number of crashes or reduce their severity. The leading cause of fatal occupational injuries in the United States in 2018 was traffic roadway crashes. The Agriculture, Forestry, and Fishing (AgFF) sector of the American economy is one of the most hazardous sectors, as evidenced by extremely high occupational injury rates in the United States. Transportation-related injuries make a large contribution to the high injury rates. Two-thirds of fatal occupational injuries in the AgFF sector were from transportation incidents (67%) in Louisiana in 2018. The logging industry is a part of the AgFF sector and is one of the most dangerous commercial enterprises in the United States. Logging workers suffered the highest number of fatal occupational injuries in the United States from 2006 to 2020. However, limited data and research are available on transportation-related injuries in the logging industry in the United States and Louisiana, specifically. This dissertation utilized unordered discrete outcome models to identify factors associated with more severe crashes. Large logging truck-related single-vehicle and multiple vehicles crashes were estimated separately. The outcome for both single-vehicle and multi vehicle crashes was defined as higher injury severity (i.e., fatal, severe, or moderate) versus lower injury severity (i.e., possible injury, no injury, or property damage). Adjusted odds ratios and 95% confidence intervals were used for the interpretation of the final model. Along with recommendations to improve the safety of logging transportation, this dissertation offer suggestions to modify the crash data collection forms and definitions to improve the classification of cargo body type and avoid misclassification of large logging truck crashes. The findings of this study also provide recommendations for future research