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A Tracking-based Traffic Performance measurement System for Roundabouts/Intersections

A Tracking-based Traffic Performance measurement System for Roundabouts/Intersections. PI: Hua Tang Graduate students: Hai Dinh Electrical and Computer Engineering Department University of Minnesota Duluth. Proposed FY11 project.

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A Tracking-based Traffic Performance measurement System for Roundabouts/Intersections

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  1. A Tracking-based Traffic Performance measurement System for Roundabouts/Intersections PI: Hua Tang Graduate students: Hai Dinh Electrical and Computer Engineering Department University of Minnesota Duluth

  2. Proposed FY11 project • The goal is to develop a tracking-based traffic performance measurement system for roundabouts and intersections • Performance measurements are in great need for roundabouts and intersections, which are more challenging than highways (more complex measurement specifications and vehicle behavior) • Many traditional techniques (using sensors) can only obtain partial performance measurements [1] T. Kwon, “Portable Cellular Wireless Mesh Sensor Network for Vehicle Tracking in an Intersection”, Minnesota Department of Transportation, Final report, CTS 08-29. [2] A. Abdel-Rahim, B. Johnson, “An Intersection Traffic Data Collection Device Utilizing Logging Capabilities of Traffic Controllers and Current Traffic Sensors”, National Institute for Advanced Transportation Technology, Final report.

  3. Basic project information • Traffic performance measurements can be processed offline • Traffic performance measurements for roundabouts and intersections include: • vehicle volume • vehicle speed (including acceleration/de-acceleration behavior) • origin-destination pairs • waiting time • gap size (by turning or entering vehicles) • lane use, etc • To apply video/image processing techniques based on probabilistic tracking algorithms to individually track each vehicle from its entrance to exit

  4. Step 1: camera calibration Actual camera height: 51 feet Calibrated camera height: 52.4 feet

  5. The height of the camera is 51 feet, which is close to our result (52.36 feet). The height of the camera is 51 feet, which is close to our result (52.36 feet). Step 2: vehicle tracking for trajectory

  6. The height of the camera is 51 feet, which is close to our result (52.36 feet). The height of the camera is 51 feet, which is close to our result (52.36 feet). Step 3: data mining for measurements

  7. The height of the camera is 51 feet, which is close to our result (52.36 feet). The height of the camera is 51 feet, which is close to our result (52.36 feet). Results I

  8. The height of the camera is 51 feet, which is close to our result (52.36 feet). The height of the camera is 51 feet, which is close to our result (52.36 feet). Results II

  9. The height of the camera is 51 feet, which is close to our result (52.36 feet). The height of the camera is 51 feet, which is close to our result (52.36 feet). Computing gap size • For each vehicle, detect when it enters • Record the position of each other vehicle • Compute the distance or travel time

  10. Summary and plan • We have tested more than 50 videos so far. • The processing time to video length ratio is about 2. • The vehicle count accuracy and gap size accuracy are over 95% and waiting time accuracy are over 92%. • The system is subject to more test (origin-destination pairs) and needs to be improved for better accuracy.

  11. The height of the camera is 51 feet, which is close to our result (52.36 feet). The height of the camera is 51 feet, which is close to our result (52.36 feet). A commercial system

  12. The height of the camera is 51 feet, which is close to our result (52.36 feet). The height of the camera is 51 feet, which is close to our result (52.36 feet). Another commercial system

  13. Acknowledgement • NATSRL • Dr. Eil Kwon (NATSRL) • District I office • MN-DoT

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