Trucking industry thrives on just-in time management, efficient routing and less travel delays. While traffic congestion continues to be a significant ‘highway’ problem, delays in truck travel cause loss of revenue to the trucking companies. Truck travel time performance measures assist in understanding the level of “truck-exclusive” congestion to plan for better routing. The truck travel times and routing strategies depend on the on-network (road) characteristics and off-network (land use and demographics) characteristics within the vicinity of roads. The literature documents limited to no research dedicated to truck travel time performance measures or their association with on-network and off-network characteristics.
The main goal of this dissertation is to research truck travel patterns, recommend performance measures, identify chokepoints, and understand the influence of on-/off-network characteristics on truck congestion. The first part of the research focuses on examining truck travel time data to choose performance measures, and understand their relationship with on-network and off-network characteristics. These performance measures are visualized geospatially to locate the chokepoints. The second part of the research focuses on the truck travel time estimation models using the on-network and off-network characteristics as the independent variables. The methodology and findings assist in locating chokepoints and prioritizing areas for truck travel improvement. The models help to estimate truck travel times and proactively plan land use or transportation network improvements.