Investigating Inductive Loop Signature Technology for Statewide Vehicle Classification Counts

Details

• Creators:
  Liao, Chen-Fu
• Corporate Creators:
  University of Minnesota. Department of Mechanical Engineering
• Corporate Contributors:
  Minnesota. Department of Transportation. Local Road Research Board ; Minnesota. Department of Transportation. Research Services & Library
• Subject/TRT Terms:
  Accuracy Automatic Vehicle Classification Data Collection Loop Detectors Traffic Counting Stations Validation
• Publication/ Report Number:
  MN/RC 2018-31, CTS#2018006
• Resource Type:
  Tech Report
• Geographical Coverage:
  Minnesota ; United States
• Edition:
  Final Report
• Corporate Publisher:
  Minnesota. Department of Transportation
• Abstract:
  An inductive loop signature technology was previously developed by a US Department of Transportation (DOT) Small Business Innovation Research (SBIR) program to classify vehicles along a section of the roadway using existing inductive loop detectors installed under the pavement. It was tested and demonstrated in California that the loop signature system could obtain more accurate, reliable and comprehensive traffic performance measures for transportation agencies. Results from the studies in California indicated that inductive loop signature technology was 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 study aimed to take advantage of the outcomes from the loop signature development to validate the performance with ground truth vehicle classification data in the Twin Cities Metropolitan Area (TCMA). Based on the results from individual vehicle class verification, class 2 vehicles had the highest match rate of 90%. Possible causes of classification accuracy for other vehicle classes may include types of loops, sensitivity of inductive loops that generates a shadow loop signal on neighboring lanes, and classification library that was built based on California data. To further understand the causes of loop signature performance and improve the classification accuracy, the author suggests performing additional data verification at a permanent Automatic Traffic Recorder (ATR) site. There is also an opportunity to investigate the classification algorithm and develop an enhanced pattern recognition methodology based on the raw loop signature profile of various types of vehicles in Minnesota.
• Format:
  PDF
• Funding:
  (C) 1003325 (WO) 27
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:3dcaf26f8e56198471c2f9383c887fb28a4b1884eaac33c5d59d1932a9146a38
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/64623/dot_64623_DS1.pdf
• File Type:
  [PDF - 9.47 MB ]