Details:

Creators:
Tsai, James ; Wu, Yiching ; Shen, Junan
Tsai, James ; Wu, Yiching ; Shen, Junan Less -
Corporate Creators:
Georgia Transportation Institute ; Georgia Institute of Technology
Georgia Transportation Institute ; Georgia Institute of Technology Less -
Corporate Contributors:
United States. Department of Transportation. Research and Innovative Technology Administration
Subject/TRT Terms:
[+]
Algorithms Cameras Gates Harbors Port Operations Ports Service Time Traffic Congestion Traffic Monitoring Traffic Surveillance Trucking Truck Traffic
Publication/ Report Number:
GTI-09-03
Resource Type:
Research Paper
Geographical Coverage:
Georgia
Corporate Publisher:
Georgia Transportation Institute University Transportation Center
NTL Classification:
NTL-FREIGHT-Trucking Industry;NTL-MARINE/WATERWAYS TRANSPORTATION-Ports;NTL-OPERATIONS AND TRAFFIC CONTROLS-Congestion;
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 lane-based 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 day and 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.
Format:
PDF
Collection(s):
US Transportation Collection University Transportation Centers
Main Document Checksum:
[+]
urn:sha256:52c10b0e55493d171d3094da43e675014f60237fb2d45cb3d4cc9ec0f43a3da9
Download URL:
https://rosap.ntl.bts.gov/view/dot/25782/dot_25782_DS1.pdf
File Type:

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