Weigh-In-Motion Station Monitoring and Calibration Using Inductive Loop Signature Technology

Despite heavy vehicles representing a small portion of vehicles on the roads, they have significant influences on pavement, safety, environment, energy consumption, and the performance of traffic system. Weigh-In-Motion (WIM) is the major technology employed to collect truck data on the freeways over three decades. However, WIM stations usually are not calibrated in a timely fashion and the calibration is mainly performed using five-axle single-trailer trucks once every half a year to three years. A potential solution is to adopt a comprehensives remote calibration monitoring system. Therefore, this study proposed an inductive loop signature-WIM based approach, which utilized both inductive loop signatures and WIM data to track heavy vehicles at WIM stations and generated "Matched Vehicle Pairs (MVPs)" for WIM station monitoring and calibration. The algorithm was established based on a previously developed truck tracking algorithm, RTREID-2MT, and integrated with a Bayesian reidentification model to filter out the MVPs that were most likely incorrectly matched by the system. The MVPs were then utilized for WIM station monitoring and temporary approximate calibration applications. Case study showed that the upstream station reported low weights, while the downstream station reported high axle spacings. The average offsets of the drive tandem axle spacing, Gross Vehicle Weight (GVW), and steer axle weight between the stations were thus derived from MVPs on a per lane basis and successfully applied to calibrate the problematic stations.