Master of Science / Department of Civil Engineering / Sunanda Dissanayake / One-ninth of all traffic fatalities in the United States (U.S.) in the past five years have involved large trucks, although large trucks contributed to only 3% of registered vehicles and 7% of vehicle miles traveled. This crash overrepresentation indicates that truck crashes in general tend to be more severe than other crashes, though they constitute a smaller portion 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, and 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 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.
| Identifer | oai:union.ndltd.org:KSU/oai:krex.k-state.edu:2097/6820 |
| Date | January 1900 |
| Creators | Bezwada, Nishitha Naveen Kumar |
| Publisher | Kansas State University |
| Source Sets | K-State Research Exchange |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.003 seconds