USE OF WIDE-AREA MOTION IMAGERY (WAMI) FOR TRANSPORTATION PLANNING AND OPERATIONS

Wide-area motion imagery (WAMI), in combination with PVLabs’ integrated Tactical Content Management System spatio-temporal capability, automatically identifies and captures every vehicle in the video view frame, storing each vehicle with a discrete ID, track ID, and time-stamped location. This unique data capture provides comprehensive vehicle trajectory information for an extended period of time, approximately three continuous hours, over a relatively large area, approximately four square miles. This report presents the results of an initial exploration of the use of these data to support transportation planning and operation activities. A subset of the data was extracted, cleaned, validated, and processed for use in calibrating car-following submodels for use in microscopic simulations. A flexible multi-dimensional filtering method was developed to evaluate and filter the data provided by PVLabs to extract valid vehicle tracks. A 10-minute sample of tracks was validated using imagery frames from the video. Resulting tracks were map-matched to roads and individual lanes to support macro and microscopic traffic characteristic extraction. A spatio-temporal trajectory data model was developed to efficiently store vehicle tracks along with the traffic model attributes required for modeling car following behavior. The final processed dataset includes all vehicles and their trajectories for an area of approximately 4-square miles that includes a dense and complex urban network of roads over a three-hour period. Several car-following and lane-changing models were reviewed in detail as were calibration and validation of these models. Both a global deterministic and stochastic calibration process were applied to a 15-minute period for the portions of tracks that occurred on two principle one-way arterials. The preliminary results indicated that the global deterministic calibration produced results similar to previous work. Results of the stochastic calibration showed a significant reduction in the variance from the initially assumed value indicating that the parameters are converging to an expected distribution and have the potential to improve model performance for the test conditions.

• Record URL:
  http://www.mautc.psu.edu/docs/VT-2013-03.pdf
• 
• Supplemental Notes:
  • This research was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Virginia Polytechnic Institute and State University, Blacksburg

  Virginia Tech Transportation Institute
  3500 Transportation Research Plaza
  Blacksburg, VA  United States  24061

  Mid-Atlantic Universities Transportation Center

  Pennsylvania State University
  201 Transportation Research Building
  University Park, PA  United States  16802-4710

  Research and Innovative Technology Administration

  University Transportation Centers Program
  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590
• Authors:
  • Hancock, Kathleen L
  • Islam, Md Rauful
• Publication Date: 2016-2

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Figures; Maps; Photos; References; Tables;
• Pagination: 73p

Subject/Index Terms

• TRT Terms: Calibration; Car following; Lane changing; Macroscopic traffic flow; Microscopic traffic flow; Traffic models; Validation; Vehicle trajectories
• Identifier Terms: Tactical Content Management System
• Uncontrolled Terms: Data filtering; Spatiotemporal data; Wide-area motion imagery
• Subject Areas: Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01612170
• Record Type: Publication
• Report/Paper Numbers: VT 2013-03
• Contract Numbers: DTRT12-G-UTC03
• Files: UTC, TRIS, RITA, ATRI, USDOT
• Created Date: Sep 27 2016 8:59PM