Investigation of speed estimation using single loop detectors.

Details

• Creators:
  Guo, Jinhua. ; Xia, Jingxin. ; Smith, Brian L., 1967-
• Corporate Contributors:
  University of Virginia. Center for Transportation Studies ; United States. Department of Transportation. Research and Innovative Technology Administration ; Mid-Atlantic Universities Transportation Center
• Subject/TRT Terms:
  • Traffic speed measurement
  • Instruments for measuring speed or velocity
  • Loop detectors
  • Single loop
  • Kalman filter
  • Traffic operations
  • Intelligent transportation systems
• Publication/ Report Number:
  Research report (University of Virginia. Center for Transportation Studies) ; no. UVACTS-14-5-126.
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  University of Virginia. Center for Transportation Studies
• Abstract:
  The ability to collect or estimate accurate speed information is of great importance to a large number of ; Intelligent Transportation Systems (ITS) applications. Estimating speeds from the widely used single ; inductive loop sensor has been a difficult, yet important challenge for transportation engineers. Based on ; empirical evidence observed from the sensor data from two metropolitan regions in Northern Virginia and ; California, this research effort developed a Kalman filter model to perform speed estimation for congested ; traffic. Taking advantage of the coexistence of dual loop and single loop stations in typical freeway ; management systems, a calibration procedure was proposed for seeding and initiating the algorithm. ; Empirical evaluation showed that the proposed algorithm can produce accurate speed estimates (on the order ; of 1-3 miles/hour error) under congested traffic conditions.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/16553/dot_16553_DS1.pdf
• File Type:
  [PDF - 203.48 KB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:f096af985e05a2872b7be14658f9444d5e0210b23eca40dde78e3d69da445eaa5f6520a4da19893483f4d0b8527ae993b0ce9bc79bef27d6955d7aca5937a478