Maximizing Port and Transportation System Productivity by Exploring Alternative Port Operation Strategies
Truck delays at terminal gates decrease productivity of ports and truck fleets (e.g., trucker idle time) and increase truck emissions. U.S. ports have explored solutions, such as gate appointment systems, to improve gate operations and reduce truck delays. It is important to quantitatively evaluate these solutions and to understand their impact on the underlying behavior (e.g., truck arrival pattern) using actual, measured gate performance data. However, researchers have difficulty obtaining measured, detailed gate performance data (e.g., for each truck) by visually reviewing images because the process is labor-intensive, time-consuming, and costly. Thus, gate performance data collection is often limited to a short period of time (only a few hours), which limits the understanding of the performance of the actual gate system. Therefore, an effective means to collect detailed gate performance data over a longer time period (e.g., one week) using images widely available is needed. A vision-based sensing system is proposed to automatically extract port gate performance data. The development of the sensing system is divided into two phases. In Phase 1, an image processing algorithm, integrating a frame-change motion detection model that takes into account color distortion to address the unique characteristics of the port gate environment, was developed to extract the service time using images widely available from surveillance cameras at the gates to demonstrate the feasibility of the proposed sensing system. The actual images acquired from surveillance camera systems at the port gate will be used to evaluate the performance of the developed algorithm in the next phase. The developed algorithm has a great potential to be used at other ports for the service time data collection since the surveillance camera systems are widely available. In Phase 2, a vision-based sensing system, including an enhanced image processing algorithm and a multi-camera system, will be developed to collect truck arrival time, wait time, and service time at the gate. In addition, the research team is in discussions with the Georgia Ports Authority to conduct an experimental test at the Port of Savannah to test the vision-based system. The detailed gate operation at Savannah Port was reviewed in Phase 1 to support the multi-camera design, and a multi-camera system was designed to collect the images for capturing the truck movement at various critical locations (portal, pedestal and inspection canopy) at the gate in support of the development and validation of the enhanced image processing algorithm.
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- Record URL:
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Supplemental Notes:
- This research was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
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Corporate Authors:
Georgia Transportation Institute University Transportation Center (GTI-UTC)
Georgia Institute of Technology
790 Atlantic Drive
Atlanta, GA United States 30332 -
Authors:
- Tsai, James
- Publication Date: 2010-6-7
Language
- English
Media Info
- Media Type: Web
- Edition: Research Report
- Features: Figures; Photos; References; Tables;
- Pagination: 42p
Subject/Index Terms
- TRT Terms: Algorithms; Cameras; Data collection; Flight delays; Image processing; Ports; Traffic surveillance; Trucking
- Identifier Terms: Port of Savannah
- Uncontrolled Terms: Gate operations; Vision based sensing systems
- Subject Areas: Data and Information Technology; Freight Transportation; Highways; Marine Transportation; Operations and Traffic Management; Terminals and Facilities; I72: Traffic and Transport Planning; I73: Traffic Control;
Filing Info
- Accession Number: 01164116
- Record Type: Publication
- Report/Paper Numbers: Report 09-03
- Contract Numbers: DTRT07G0051
- Files: UTC, NTL, TRIS
- Created Date: Jul 28 2010 4:03PM