Smart and Cooperative Truck Parking Monitoring and Calibration System Empowered by Machine Learning

The primary goal of this research is to create a truck parking monitoring and calibration system empowered by machine learning, for the in/out truck parking counting system. Four components will be integrated into the proposed system, including (1) the truck parking lot sensing component, (2) the information collection component, (3) the sensing anomaly detection component, and (4) the cooperative monitoring and calibration component. Counting data can be obtained by the installed sensors, i.e., radars, cameras, and etc., at the entrance and exit at a truck parking lot. A separate video surveillance system will also be installed to collect ground-truth data. Then, the real-time parking occupancy can be calculated, and the anomaly status can be identified based on the comparison with ground-truth records. The next step is to send the collected information, such as real-time or historical ground-truth occupancy sequence and the anomaly detection results, into the cooperative calibration component. Finally, the proposed system can generate the calibrated occupancy result, confidence rate, sensing system status and calibration recommendations. To achieve this goal, the research team has identified four objectives: (1) propose a real-time truck parking sensing system status anomaly detection framework; (2) propose a real-time parking lot occupancy estimation empowered by deep learning; (3) propose a human-machine cooperative monitoring and calibration algorithm empowered by sequence classification; and (4) build a live website to visualize the truck parking sensing monitoring and calibration result.

Language

• English

Project

• Status: Active
• Funding: $100000
• Contract Numbers:

  1461-97

• Sponsor Organizations:

  Washington State Department of Transportation

  Research Office
  P.O. Box 47372
  Olympia, WA  United States  98504
• Project Managers:

  Brodin, Doug

• Performing Organizations:

  University of Washington, Seattle

  Civil and Environmental Engineering Department
  201 More Hall, Box 352700
  Seattle, WA  United States  98195-2700
• Principal Investigators:

  Wang, Yinhai

• Start Date: 20220916
• Expected Completion Date: 20231231
• Actual Completion Date: 0

Subject/Index Terms

• TRT Terms: Data collection; Machine learning; Parking guidance systems; Sensors; Trucks
• Subject Areas: Freight Transportation; Motor Carriers; Operations and Traffic Management;

Filing Info

• Accession Number: 01863241
• Record Type: Research project
• Source Agency: Washington State Department of Transportation
• Contract Numbers: 1461-97
• Files: RIP, STATEDOT
• Created Date: Nov 1 2022 3:48PM