Automated Data Collection for Origin/Destination Studies of Freight Movement, Phase 2
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Automated Data Collection for Origin/Destination Studies of Freight Movement, Phase 2

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    Origin and destination (O–D) include the start and end points and times of any vehicular trip. These data are valuable to traffic modelers and transportation planners. The collection of O–D data usually comes from surveys, visual counts, classifier counts, or other methods. These methods of collection tend to be expensive and time consuming. The aim of this research project was to develop a novel method of automated real-time O–D data collection that is reliable, inexpensive, and portable, using a mix of commercial off-the-shelf hardware and custom software. As such, the researchers conducted an automated license plate reading methodology. The first step was to identify a length of highway on which cameras could be installed such that license plates would be in view and three stations could be set up to get the maximum interpretation of origins and destinations. The second step included selection of the appropriate hardware configuration (i.e., camera and trigger systems) and a solar power design for each location that would be cost effective and safe. The third step was the installation of the hardware and solar power components. The final step was the development of software to process and interpret the collected O–D data. The initial plan was to use triggering devices to detect trucks and take snapshots of the rear license plate. This proved to be quite difficult due to fluctuations in the speed and lengths of the trucks. Also, truck tires often direct dirt and debris onto license plates, rendering them unreadable. The solution was to turn the cameras around and capture the front license plates. The plates would be processed by optical character recognition (OCR) software, and the results of the OCR would be stored in a database. The results were then analyzed using database and pattern-matching techniques to show when a truck entered and left this transportation network. The researchers utilized a three-step approach to match license plate reads between locations. The first attempt to match was a simple database query based on exact matches within a selected time frame. The second attempt was to use a fuzzy match using database queries with wildcards and substitutions for common OCR misreads. The third attempt used partial matches via coded algorithms. Any reads that were unmatched were assumed to have entered the network between locations. The findings from the University of Central Florida’s (UCF’s) Location 3 two-week data collection indicated that historical traffic data collected by FDOT in 2015 is consistent with the UCF system in terms of weekday traffic trends. Traffic counts collected by the UCF system at Location 3 were within 107 percent of FDOT’s weekday average, and truck counts were within approximately 109 percent of FDOT’s average in year 2015.
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