Gingerich, KevinAli, Syed Ubaid Ullah2020-11-132020-11-132020-092020-11-13http://hdl.handle.net/10315/37970A route choice model is developed in this thesis to explain and predict long-haul truck vehicle movements in Ontario. An algorithm is devised to process approximately 58,000 observed trips from GPS data in ArcGIS to establish variable choice sets based on an optimal commonality factor that measures route overlap. Novel implications of the commonality factor add to the existing literature. Route characteristics are next used to estimate a C-logit model. Results indicate that truck drivers are more likely to select routes exhibiting lower minimum travel times, more freeway and Highway 401 usage, more diesel stations, and fewer intersections. The travel time is the most dominant variable based on measurements of elasticity. Two scenarios are tested using the final model to determine routing changes due to increased travel time on Highway 401 and other freeways. Further detailed scenarios can be used to predict long-haul trucking patterns for future transportation planning purposes.Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Civil engineeringElectronic Thesis or Dissertation2020-11-13Route choiceLong-haul truck tripsLogit modelCommonality factorChoice Set GenerationGPS dataTruck Route ChoiceRoute Choice models