Modeling Road Link-Level Truck Travel Times Using On- and Off-Network Characteristics

Abstract

Travel times of freight trucks (or trucks) play a major role in trip planning, identification of efficient routes, and allocating resources for implementation of strategies. While research on travel time estimation models for passenger cars or traffic stream is documented in the literature, their applicability for trucks remains debatable. Truck travel is influenced by the road characteristics, surrounding land use, and demographics of an area. The focus of this research, therefore, is on estimating the truck travel times using the on-network (road) and off-network (land use and demographics) characteristics. Truck travel time data for 501 road links in North Carolina from 2019 were processed for four times of the day and two days of the week. Generalized estimating equations (GEE) were used with the average truck travel time per mile (ATTTPM) as the dependent variable. Spatial proximity (buffers of 0.25 mi, 0.50 mi, and 1 mi) and spatial weights (distance decay functions like 1/d, 1/d2, and 1/d3) were explored to check the best possible approach for capturing off-network characteristics. The office (business park and administrative areas), transportation (rest areas and parking facilities), heavy commercial, and light industrial land uses have an increasing influence on the ATTTPM. The linear model developed using data from a 0.25-mi buffer is best suited to estimate the ATTTPM. Off-peak hour delivery incentives or truck priority systems on road links near these land uses can be implemented to improve truck mobility. The methodology illustrated in this research is transferable and could be used for estimating truck travel times across a region.