Freeway ramp control using fuzzy set theory for inexact reasoning |
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| Institution: | 1. Faculty of Medicine, University of Iceland, Reykjavík, Iceland;2. Division of Pulmonary and Sleep Medicine, Nemours Children''s Hospital, Orlando, Florida;3. Division of Allergy/Immunology and Pulmonary Medicine, Duke University School of Medicine, Durham, North Carolina;1. Department of Microbiology and Immunology, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA;2. Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL, USA;3. Department of Obstetrics & Gynecology, Advocate Lutheran General Hospital, Park Ridge, IL, USA;1. Department of Cellular Biotechnologies and Hematology, Sapienza University of Rome, Italy;2. Dipartimento di Scienze Ginecologico-Ostetriche e Scienze Urologiche, Sapienza University of Rome, Italy;3. Institute of Cell Biology and Neurobiology (IBCN-CNR), Rome, Italy;4. Centro Riferimento Alcologico Regione Lazio, Sapienza University of Rome, Italy;5. Centro Nazionale Sostanze Chimiche, ISS, Rome, Italy;6. Institute of Translational Pharmacology (IFT-CNR), Italy;1. Cincinnati Children''s Hospital Medical Center, 3333 Burnet Avenue, ML 7035, Cincinnati, OH 45229, USA;2. University of Rochester School of Medicine and Dentistry, Saunders Research Bldg, Box 694, Rochester, NY 14642, USA |
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| Abstract: | This paper presents a fuzzy controller for freeway ramp metering, which uses rules of the form: IF “freeway condition” THEN “control action.” The controller has been designed to consider varied levels of congestion, a downstream control area, changing occupancy levels, upstream flows, and a distributed detector array in its rule base. Through fuzzy implication, the inference of each rule is used to the degree to which the condition is true. Using a dynamic simulation model of conditions0fj at the San Francisco-Oakland Bay Bridge, the action of the fuzzy controller is compared to the existing “crisp” control scheme, and an idealized controller. Tests under a variety of scenarios with different incident locations and capacity reductions show that the fuzzy controller is able to extract 40 to 100% of the possible savings in passenger-hours. In general, the fuzzy algorithm displays smooth and rapid response to incidents, and significantly reduces the minute-miles of congestion. |
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