Extended Kalman Filter for the On-line Calibration of Traffic Simulation Models

Details

• Creators:
  Sadek, Adel W ; Rizzo, Donna M.
• Corporate Creators:
  University of Vermont. Department of Civil and Environmental Engineering ; New England University Transportation Center ; Massachusetts Institute of Technology
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Calibration
  • Kalman filtering
  • Loop detectors
  • Neural networks
  • Traffic data
  • Traffic simulation
• Publication/ Report Number:
  Project No. UVMR16-7
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report
• Corporate Publisher:
  New England University Transportation Center ; Massachusetts Institute of Technology
• Abstract:
  The main purpose of this research was to develop methods and procedures for using the on-line data collected by field traffic detectors in the calibration of simulation models, whether they are to be used for off-line analyses or on-line traffic state estimation. Initially, the study’s goal was to use an Extended Kalman Filter to achieve this objective. Later however, it was decided to use an Artificial Neural Network (ANN) to act as a post-processing algorithm that would bring the simulation model’s predictions closer to real-world observations.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/65870/dot_65870_DS1.pdf
• File Type:
  [PDF - 51.78 KB]
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:fe92e3fb3ffb10285efbe2a685394083e3cfb22b89593cfe1981a0e2cafb1b28ce8b3a67c7f3dd8549fe89006216afabfc4e87c67266b970c6b97ad7ad9c4426