Exposure modelling of productivity-permitted general freight trucking on uncongested highways

The research designs, develops, validates, and applies an exposure model of productivity-permitted general freight trucking on uncongested highways. Productivity-permitted general freight trucks (long trucks) are multiple trailer configurations, consisting of van trailers, which exceed basic vehicle length limits but operate within basic weight restrictions. The three predominant long trucks in North America are Rocky Mountain doubles (Rockies), Turnpike doubles (Turnpikes), and triple trailer combinations (triples). Long trucks have been used in Canada since the late 1960s. Recent highway investments in the Canadian Prairie Region have effectively completed the network on which long trucks are allowed to operate. Despite widespread use of long trucks for many years and these recent infrastructure investments, there is a knowledge deficiency about long truck exposure. The research uses the transportation systems analysis approach to design, develop, and validate the long truck exposure model. Exposure is expressed as an explanatory variable in three principal dimensions (volume, weight, and cube), which is needed for predicting transportation system impacts of long truck operations. The research applies the model to clarify issues that should be considered in establishing charges for long truck permits, determining long truck safety performance, and developing load spectra for long trucks. The exposure model relies on a unique dataset that integrates output from a classification algorithm, field observations, and industry intelligence. The results indicate that long trucks travelled 67 million kilometres on a 10,000 centreline-kilometre highway network in the Canadian Prairie Region in 2006. The model demonstrates strong temporal and geographic concentration of long truck travel on the network. Application of the results reveals the following findings: • Decisions about establishing long truck permit charges are supported by consideration of options within a revenue