Improving rush hour traffic flow by computer-vision-based parking detection and regulations

Parking is critical to transportation system, however, the impact of parking management to traffic congestion has long been overlooked, partially due to the lack of high granular parking data. Real-time monitoring parking occupancy at the block level for the entire city is oftentimes infeasible because parking sensors are costly. This research will use computer vision techniques to provide ubiquitous and cheap sensing for detecting parking occupancy in high granularity. This would enable efficient management of parking infrastructure, which leads to mitigate traffic congestion, enhance transportation infrastructure resilience, and reduce environmental impacts. The research team will use W. Liberty Ave in Dormont as the test site, parking on this street is prohibited during rush hour and compliance of motorists have a significant impact on congestion. Parking occupancy will be measured by analyzing videos from vehicles that traveled on this road. Developing this detector will be one of the main tasks of this project. Combining this data with traffic flow data and other information will let us analyze impact on traffic of parking restrictions and determine the optimal trade-off between parking availability and traffic flow. This will be integrated into a general parking and roadway information system and provide Dormant with an efficient strategy to manage parking along the W. Liberty Ave corridor.

Language

  • English

Project

  • Status: Active
  • Funding: $90000
  • Contract Numbers:

    69A3551747111

  • Sponsor Organizations:

    Carnegie Mellon University

    Mobility 21 National UTDOT for Mobility of Goods and People
    ,    

    Office of the Assistant Secretary for Research and Technology

    University Transportation Program
    ,    
  • Managing Organizations:

    Carnegie Mellon Univeristy

    Mobility 21 National UTDOT for Mobility of Goods and People
    ,    
  • Project Managers:

    Schweyer, Lisa Kay

  • Performing Organizations:

    Carnegie Mellon University

    ,    
  • Principal Investigators:

    Mertz, Christoph

  • Start Date: 20180701
  • Expected Completion Date: 20190630
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01677513
  • Record Type: Research project
  • Source Agency: Technologies for Safe and Efficient Transportation University Transportation Center
  • Contract Numbers: 69A3551747111
  • Files: UTC, RiP
  • Created Date: Aug 7 2018 12:04PM