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

摘要

The main objective of this project is to evaluate the feasibility of re-identifying commercial trucks based on vehicle-attribute data automatically collected by sensors installed at traffic data collection stations. To support this work, archived data from weigh-in-motion (WIM) stations in Oregon are used for developing, calibrating, and testing vehicle re-identification algorithms. The vehicle re-identification methods developed in this research consist of two main stages. In the first stage, each vehicle from the downstream station is matched to the most similar upstream vehicle by using a Bayesian model. In the second stage, several methods are introduced to screen out those vehicles that cross the downstream site but not the upstream site and to tradeoff accuracy versus the total number of vehicles being matched. These methods involve calculating both the highest and the second highest similarity measures for each vehicle being matched. It is demonstrated that the proposed screening approach improves the accuracy of the re-identification methods significantly. The models are applied to the truck data collected by WIM sensors at three stations in Oregon, which together create two different links that are 125 and 145 miles long, respectively.