Investigating inductive loop signature technology for statewide vehicle classification counts

Principal Investigator:

Chen-Fu Liao, Senior Systems Engineer, Mechanical Engineering

Project Summary:

In 2013, the United States Department of Transportation (USDOT) sponsored a study that used inductive loop signatures from existing Inductive Loop Detectors (ILD) installed under the pavement to obtain more accurate, reliable, and comprehensive traffic performance measures for transportation agencies. Results from the study indicated that inductive loop signature technology is able to re-identify and classify vehicles along a section of roadway and provide reliable performance measures for assessing progress at the local, state, or national level. This research is taking advantage of the outcomes from the loop signature development to validate the performance with ground truth vehicle classification data. The project goal is to evaluate the accuracy and reliability of using the single loop detector signature for vehicle classification under different traffic conditions. There are likely opportunities to convert current traffic volume counters (ATR/volume) into volume and classification stations using existing inductive loop detectors. The technology has the potential to save lots of time and money and could provide the Minnesota Department of Transportation (MnDOT) more data--especially in the metro area where loop detectors are already installed on freeways, ramps, and at traffic signals. The objective of this proposal is to leverage existing loop detectors for vehicle classification counts. If successful, the technology can save lots of time and money, and could provide state, county, or city more data--especially in the metro area where loop detectors are already installed.

Sponsors:

Project Details:

  • Start date: 08/2017
  • Project Status: Active
  • Research Area: Transportation Safety and Traffic Flow
  • Topics: Data and modeling