Principal Investigator: Sunanda Dissanayake
Sponsors & Partners
Mid-America Transportation Center
Kansas State University Department of Civil Engineering
About this Project
Brief Project Description & Background
The project will gather crash data related to large trucks, which will be analyzed and modeled to identify characteristics and contributory causes. Based on that, countermeasure ideas and focus areas needing particular attention for improving highway safety situation of large trucks will be suggested.
To identify characteristics and contributory causes related to large trucks. To recommend countermeasure ideas and focus areas needing particular attention for improving the safety situation of truck related crashes.
Upon completion of the project, the characteristics and contributory causes of the large truck related crashes will be identified, which will in turn be used to recommend countermeasure ideas and focus areas needing particular attention for improving the safety situation of truck related crashes.
One-ninth of all traffic fatalities in the United States have involved large trucks in the past five years, although large trucks contributed to only 3% of registered vehicles and 7% of vehicle miles travelled. This contrasting proportion indicates that truck crashes in general tend to be more severe than other crashes, though they constitute a smaller sector of vehicles on the road. To study this issue, fatal crash data from the Fatality Analysis Reporting System (FARS) was used to analyze characteristics and factors contributing to truck-involved crashes. Driver, vehicle and crash-related contributory causes were identified. As an extension, the likelihood of occurrence of these contributory causes in truck-involved crashes with respect to non-truck crashes was evaluated using the Bayesian Statistical approach. Likelihood ratios indicated that factors such as stopped or unattended vehicles and improper following have greater probability of occurrence in truck crashes than in non-truck crashes. Also, Multinomial Logistic Regression was used to model the type of fatal crash (truck vs. non-truck) to compare the relative significance of various factors in truck and non-truck crashes.
Factors such as cellular phone usage, failure to yield right-of-way, inattentiveness and failure to obey traffic rules also have a greater probability of resulting in fatal truck crashes. Among several other factors, inadequate warning signs and poor shoulder conditions were also found to have greater predominance in contributing to truck crashes than non-truck crashes. By addressing these factors through the implementation of appropriate remedial measures, the truck safety experience could be improved, which would eventually help in improving overall safety of the transportation system.