Generating Reliable Freight Performance Measures with Truck GPS Data

Abstract

Building on previous efforts to study freight mobility and reliability, a truck GPS data analysis methodology was developed to study the freight performance of heavy commercial vehicles along key freight corridors in the Twin Cities metropolitan area in Minnesota. Twelve months of truck GPS data collected in 2012 were obtained from the American Transportation Research Institute. Several performance measures, such as truck mobility, delay, and reliability measures, were derived and statistically analyzed by route, roadway segment, and time of day. The derived performance measures were validated with available benchmark data to ensure data quality. Data quality validation is important particularly in urban areas where satellite reception may be limited and traffic congestion is more common. For data quality verification, average truck speed and hourly volume percentage computed from the truck GPS data were validated with data from weigh-in-motion sensors, loop detectors, and automatic traffic recorders at selected locations. The analysis methodology using truck GPS data offers potential opportunities for freight planners and managers to generate reliable measures in a timely manner. Findings from this research indicate that those measures derived from the truck GPS data can be used in supporting the U.S. Department of Transportation performance measure initiative and truck and freight modeling in metropolitan areas. The performance measures can provide truck-specific information to support regional surface freight planners in identifying freight bottlenecks and infrastructure improvement needs and in developing operational strategies to promote efficient freight movement for the industry.