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Assistant Professor Benjamin A. Coifman
Department of Civil Engineering, The Ohio State University
470 Hitchcock Hall, Columbus, OH 43210
(614) 292 4282
(614) 292 3780 fax
The goal of this research is to utilize the advances in velocity and length estimation from single loop detectors to develop a vehicle classification methodology. It is envisioned that the classification work will also improve length-based classification at dual loop detectors. The research promises to extend vehicle classification to existing stations using only single loop detectors and offers viable options in the event that one of the loops in a dual
loop detector fails.
Single loop detectors are the most common vehicle detector in use to monitor traffic, both for real time operations and for collecting census data used in management systems. New detectors may replace loop detectors, but most of these detectors emulate the operation of single loops. Collecting reliable length data from any of these single detectors is nearly impossible. Classification based solely on vehicle length, however, could be an
alternative to axle-based classification and as such this project develops new techniques for estimating velocity at a single loop detector, yielding estimates that approach the accuracy of a dual-loop detectors. The researchers therefore would closely estimate vehicle length. The resulting methodology will be tested on event data collected from the
Columbus Ohio system. ODOT’s single loop detector count stations use proprietary software and cannot be modified directly but, as part of this project, the software vendors may incorporate the classification functionality into their equipment. Several other locations could benefit from the research, including the traffic monitoring systems both in the GCM Corridor and Minneapolis/St. Paul. The classification work allows these systems to better monitor freight traffic within the metropolitan areas.
- Task 1. Meet with ODOT engineers and potentially teleconference with other state departments of transportation (DOT’s) in the MRUTC to establish properties of existing classification systems and desired properties of the classification system, e.g., number of bins and length thresholds between bins.
- Task 2. Collect additional detector data. Particular emphasis will be on stations with significant truck demand. Each location will need an external measure for verification. ODOT will be the primary source to log individual vehicle data from several of their stations. The researchers will collect concurrent video at several stations to manually verify the vehicle class.
- Task 3. Develop and test the single loop detector classification against the Weigh in Motion or axle classification. In parallel, the researchers will further improve the length estimation techniques from single loop detectors. As only a few of these detectors experience significant truck demand (i.e., over 20 percent of the flow), this area is one where the classification stations will be most helpful.
- Task 4. Use the manually extracted vehicle class from video to verify the methodology.
- Task 5. Work with ODOT and other state DOT’s to deploy the resulting length-based classification methodology for single loop detectors.
Technology Transfer Activities
None identified in proposal, but it appears that the researcher will share the resulting methodology with the states.
Potential Benefits of the Project
Offers an alternative to new and more expensive detector installations.
- Duration: 12 months
- Budget: $82,346, Matching Funds & %: $62,348 76% (311%)
- Match Source: OSU and ODOT
- Student Involvement: 1 graduate research asst
- Modal Orientation: Highways
- Project Number: 05-02