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

013353932011Final ReportPDFTech ReportOTREC-RR-11-07AccuracyAutomatic vehicle identificationFreight transportationTravel timeWeigh in motionResearch HubData and Information TechnologyFreight TransportationHighwaysI72: Traffic and Transport PlanningOregonUnited StatesPortland State UniversityMonsere, Christopher M.Mecit CetinAndrew P. NicholsOregon Transportation Research and Education ConsortiumUS Transportation CollectionThe 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 \u201csimilar\u201d 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 \u201clinks\u201d that are 125 and 145 miles long, respectively. It is observed that the algorithms can match trucks with approximately 90% accuracy while the total number of trucks being matched at this accuracy level is about 95% of the actual common trucks that cross both upstream and downstream sites. These methods allow the user to trade-off the accuracy vs. total vehicles being matched by adjusting a threshold parameter. For example, trucks can be matched with 98% accuracy if one is willing to match about 40% of all common trucks. It is also found that when travel times of vehicles between the upstream and downstream sites exhibit larger variation, mismatch rate increases. Overall, for estimating travel times and origin-destination flows between two WIM sites, the methods developed in this project can be used to effectively match commercial vehicles crossing two data collection sites that are separated by long distances