Exploratory Methods for Truck Re-identification in a Statewide Network Based on Axle Weight and Axle Spacing Data to Enhance Freight Metrics

Christopher Monsere, Portland State University


  • Mecit Cetin, Old Dominion University Civil & Environmental Engineering
  • Andrew Nichols, Marshall University


Most transportation agencies rely on point detectors (e.g., inductive loops, axle detectors) located at specific points on highways to collect data on traffic volumes, vehicle classes, and other relevant attributes of traffic. By utilizing the data collected from these point detectors, researchers have developed vehicle re-identification algorithms to match measurements at two sites that belong to the same vehicle. This enables tracking the movement of individual vehicles between different data collection sites, which in turn provides valuable information for the estimation of travel times, travel delays, and origin-destination flows. The aim of this OTREC project is to investigate the feasibility of re-identifying trucks in a statewide network by developing and applying vehicle re-identification algorithms. Data from weigh-in-motion (WIM) stations provide a basis for the development and testing of these algorithms. The data supporting this research come from the WIM sites in Oregon, which are equipped with sensors that can measure axle weights, axle spacing, and gross vehicle weight estimates that are uniquely matched to each truck. Since some of the trucks (20-35%) are carrying Green Light transponders, these measured attributes are also uniquely matched to transponder-equipped trucks. These particular trucks provide the needed data for model development, calibration, and testing.

Project Details

Project Type:
Project Status:
End Date:
January 31,2009
UTC Grant Cycle:
OTREC 2009
UTC Funding: