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Maximizing port and transportation system productivity by exploring alternative port operation strategies.
  • Published Date:
    2011-06-01
  • Language:
    English
Filetype[PDF-1.25 MB]


Details:
  • Publication/ Report Number:
    GTI-09-03
  • Resource Type:
  • Geographical Coverage:
  • NTL Classification:
    NTL-FREIGHT-Trucking IndustryNTL-MARINE/WATERWAYS TRANSPORTATION-Ports ; NTL-OPERATIONS AND TRAFFIC CONTROLS-Congestion ;
  • Format:
  • Abstract:
    Seaports are a critical transportation component that supports the nation’s economy. Many U.S.

    ports are now experiencing significant truck congestion at the gate, which decreases the productivity of

    ports and truck fleets (e.g. truck wait times) and increases vehicle exhaust emissions, which contributes to

    air pollution. Actual truck traffic data at the gate, including arrival time, service/processing time, and

    wait/queue time, is essential for studying truck congestion, but such data has been difficult to obtain with

    existing manual data collection methods. This research proposes a service time extraction algorithm using

    video log images taken by surveillance cameras at the gate to effectively acquire this much-needed data.

    A service time extraction algorithm consisting of three unique components, 1) a design of two lanebased

    regions of interest (ROIs) to represent truck trajectories, 2) a frame-differencing change detection

    algorithm addressing low frame-rate and cast shadow issues, and 3) a unique transition model with a set of

    decision rules that considers perspective occlusion and other potential sources of false positive detections,

    was developed to reliably detect truck departures. The performance of the proposed algorithm was

    evaluated using 6,567 actual images captured via internet at a low frame-rate from a live video feed from a

    gate surveillance camera in the U.S. Preliminary results demonstrate the robustness of the proposed

    algorithm by successfully detecting truck departures under various challenging conditions, including dayand-

    night lighting conditions, perspective occlusion, cast shadows, multi-lane departures, and non-truck

    movements. The algorithm achieved a correct detection rate of 98.1% for all the images, which can

    sufficiently represent truck service times at a gate. To further extend the use of this vision-based

    technology, a vision-based, multi-view gate data acquisition module is proposed to collect the images at the

    Port of Savannah for wait time extraction validation. In addition, the Georgia Institute of Technology, the

    Center of Innovation for Logistics, and private sector corporations have initiated a project to extend the

    proposed algorithm for monitoring and optimizing the flow of truck traffic in the roadway network near the

    Port of Savannah

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