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

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.

• Record URL:
  • http://utc.mit.edu/uploads/files/UVMR16-7%20Word.pdf
• Record URL:
  • https://scholarworks.uvm.edu/trc/263/
• 
• Record URL:
  • https://rosap.ntl.bts.gov/view/dot/65870
• Supplemental Notes:
  • This research was supported by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  University of Vermont, Burlington

  Department of Civil and Environmental Engineering, 33 Colchester Avenue
  Burlington, VT  United States  05405

  New England University Transportation Center

  Massachusetts Institute of Technology
  77 Massachusetts Avenue, Room 40-279
  Cambridge, MA  United States  01239
• Authors:
  • Sadek, Adel W
  • Rizzo, Donna
  • 0000-0003-4123-5028
• Publication Date: 2009-4-9

Language

• English

Media Info

• Media Type: Web
• Edition: Final Report
• Pagination: 2p

Subject/Index Terms

• TRT Terms: Calibration; Kalman filtering; Loop detectors; Neural networks; Traffic data; Traffic simulation
• Subject Areas: Highways; Operations and Traffic Management; I73: Traffic Control;

Filing Info

• Accession Number: 01132450
• Record Type: Publication
• Report/Paper Numbers: Project No. UVMR16-7
• Files: UTC, NTL, TRIS
• Created Date: Jul 9 2009 1:56PM