Refining Inductive Loop Signature Technology for Statewide Vehicle Classification Counts

Principal Investigator(s):

Chen-Fu Liao, Researcher, Mechanical Engineering

Project summary:

With increasing traffic volumes and greater restrictions on placing road tubes to perform vehicle-classification counts, it is necessary to find innovative ways to collect vehicle class data on roadways. A loop-signature technology was implemented at the California Department of Transportation that uses existing loops to obtain vehicle signatures to classify vehicles. There is an opportunity to take advantage of the latest development of the loop-signature technology and validate its performance in Minnesota.

This project builds upon previous research by performing further testing at traffic signals or automatic traffic recorder (ATR) sites to better understand loop-signature performance issues, to improve the classification accuracy, and to develop an enhanced pattern recognition based on the signature profiles of various types of vehicles in Minnesota. The objective is to convert current loop detectors at signals, on freeways, and at ATRs into classification sites using the existing loop detectors.

Loop-signature technology could be a huge innovation to replace existing data-collection methods and save the state a lot of time and money. In addition, it could provide MnDOT more and better data on ramps and freeways in the metro area where it is difficult and time-consuming to collect vehicle-classification counts.

Project details: