Implementing Inductive Loop Signature Technology for Vehicle Classification CountsStatus: CompleteReport Date: 12/18/2024 Summary: Upgraded inductive loop technology could provide valuable vehicle count and classification information from an expanded list of locations. This innovative technology leverages the use of existing inductive loops and is 95% accurate at classifying vehicle types after hardware and software upgrades. MnDOT upgraded existing inductive loops at five sites to evaluate vehicle classification accuracy. While the average accuracy was 95%, the technology was not as accurate for classifying trucks, which have similar undercarriage characteristics. However, reducing the number of vehicle classes from the Federal Highway Administration’s 13 classes to the Highway Performance Monitoring System’s seven classes resulted in a 97% accuracy in correctly classifying single trailer trucks. Additionally, the upgraded inductive loop outperformed another commercial vehicle classification system in vehicle classification (92% vs. 86%) and detection rate (100% vs. 77%). Final Deliverables: Implementation of Inductive Loop Signature Technology for Vehicle Classification Counts (Report #2024-29) An Evaluation of Vehicle Identification Technology (Research Summary) Related Materials: Related Research: Refining Inductive Loop Signature Technology for Statewide Vehicle Classification Counts Project Personnel: Principal Investigator: Gordon Parikh Co-Principal Investigator: Erik Minge, Peter Dirks Technical Liaison: Gene Hicks Project Coordinator: David Glyer Panel Members: Andrew Andrusko - Freight & Commcl Veh Ops Derek Lehrke - Metro District Ian Vaagenes - Transportation System Management John Hackett - Transportation System Management Joshua Kuhn - Transportation System Management Kevin Millage - Metro District
