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.

Many state agencies consider exclusive truck facilities to be an alternative to handle the safety and operational issues due to the increasing truck volumes. No such facilities exist, and there are no standard tools or procedures for measuring safety performance of an exclusive truck facility. This thesis aims at identifying factors that affect truck crashes, whose results could be used for better designing exclusive truck facilities. To accomplish the objectives of this thesis, five years’ roadway and crash data for Texas was collected to develop a comprehensive crash database. Negative binomial regression models were used to establish a relationship between truck crashes and various environmental, geometric and traffic variables. Separate models were developed for truck-related (involving at least one truck and another vehicle), truck-only (two trucks or more) and single-truck crashes. The results suggested that the percentage of trucks in Average Annual Daily Traffic (AADT), classification of the roadway (Rural/Urban), posted speed limit, surface condition, alignment and shoulder width are associated with truck crashes. It was observed that truck-related and truck-only crashes decreased as the percentage of trucks increased on freeway facilities. Based on conclusions derived from the literature review and statistical analyses, straight segments with wider shoulders and uniform grades are recommended for exclusive truck facilities. It is also recommended to provide ramps, horizontal and vertical curvature and signing based on truck size, driver eye height, braking ability and maneuverability. These models were developed using mixed-flow traffic data to understand the association of various factors with truck crashes. These models should not be used directly to estimate or predict truck crashes. Further analysis with more detailed data under different flow conditions might help in quantifying the safety performance of exclusive truck facilities.

Many state agencies consider exclusive truck facilities to be an alternative to handle the safety and operational issues due to the increasing truck volumes. No such facilities exist, and there are no standard tools or procedures for measuring safety performance of an exclusive truck facility. This thesis aims at identifying factors that affect truck crashes, whose results could be used for better designing exclusive truck facilities. To accomplish the objectives of this thesis, five years' roadway and crash data for Texas was collected to develop a comprehensive crash database. Negative binomial regression models were used to establish a relationship between truck crashes and various environmental, geometric and traffic variables. Separate models were developed for truck-related (involving at least one truck and another vehicle), truck-only (two trucks or more) and single-truck crashes. The results suggested that the percentage of trucks in Average Annual Daily Traffic (AADT), classification of the roadway (Rural/Urban), posted speed limit, surface condition, alignment and shoulder width are associated with truck crashes. It was observed that truck-related and truck-only crashes decreased as the percentage of trucks increased on freeway facilities. Based on conclusions derived from the literature review and statistical analyses, straight segments with wider shoulders and uniform grades are recommended for exclusive truck facilities. It is also recommended to provide ramps, horizontal and vertical curvature and signing based on truck size, driver eye height, braking ability and maneuverability. These models were developed using mixed-flow traffic data to understand the association of various factors with truck crashes. These models should not be used directly to estimate or predict truck crashes. Further analysis with more detailed data under different flow conditions might help in quantifying the safety performance of exclusive truck facilities.

Master of Science / Department of Civil Engineering / Sunanda Dissanayake / In order to improve safety of the overall surface transportation system, each of the critical areas needs to be addressed separately with more focused attention. Statistics clearly show that large-truck crashes contribute significantly to an increased percentage of high-severity crashes. It is therefore important for the highway safety community to identify characteristics and contributory causes related to large-truck crashes. During the first phase of this study, fatal crash data from the Fatality Analysis Reporting System (FARS) database were studied to achieve that objective. In this second phase, truck-crashes of all severity levels were analyzed with the intention of understanding characteristics and contributory causes, and identifying factors contributing to increased severity of truck-crashes, which could not be achieved by analyzing fatal crashes alone. Various statistical methodologies such as cross-classification analysis and severity models were developed using Kansas crash data. Various driver-, road-, environment- and vehicle- related characteristics were identified and contributory causes were analyzed. From the cross-classification analysis, severity of truck-crashes was found to be related with variables such as road surface (type, character and condition), accident class, collision type, driver- and environment-related contributory causes, traffic-control type, truck-maneuver, crash location, speed limit, light and weather conditions, time of day, functional class, lane class, and Average Annual Daily Traffic (AADT). Other variables such as age of truck driver, day of the week, gender of truck-driver, pedestrian- and truck-related contributory causes were found to have no relationship with crash severity of large trucks. Furthermore, driver-related contributory causes were found to be more common than any other type of contributory cause for the occurrence of truck-crashes. Failing to give time and attention, being too fast for existing conditions, and failing to yield right of way were the most dominant truck-driver-related contributory causes, among many others. Through the severity modeling, factors such as truck-driver-related contributory cause, accident class, manner of collision, truck-driver under the influence of alcohol, truck maneuver, traffic control device, surface condition, truck-driver being too fast for existing conditions, truck-driver being trapped, damage to the truck, light conditions, etc. were found to be significantly related with increased severity of truck-crashes. Truck-driver being trapped had the highest odds of contributing to a more severe crash with a value of 82.81 followed by the collision resulting in damage to the truck, which had 3.05 times higher odds of increasing the severity of truck-crashes. Truck-driver under the influence of alcohol had 2.66 times higher odds of contributing to a more severe crash. Besides traditional practices like providing adequate traffic signs, ensuring proper lane markings, provision of rumble strips and elevated medians, use of technology to develop and implement intelligent countermeasures were recommended. These include Automated Truck Rollover Warning System to mitigate truck-crashes involving rollovers, Lane Drift Warning Systems (LDWS) to prevent run-off-road collisions, Speed Limiters (SLs) to control the speed of the truck, connecting vehicle technologies like Vehicle-to-Vehicle (V2V) integration system to prevent head-on collisions etc., among many others. Proper development and implementation of these countermeasures in a cost effective manner will help mitigate the number and severity of truck-crashes, thereby improving the overall safety of the transportation system.

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