NDLI: A neuro-fuzzy algorithm for coordinated traffic responsive ramp metering

Content Provider	IEEE Xplore Digital Library
Author	Bogenberger, K. Keller, H. Vukanovic, S.
Copyright Year	2001
Description	Author affiliation: Fachgebiet Verkehrstechnik und Verkehrsplanung, Munich Univ. of Technol., Germany (Bogenberger, K.)
Abstract	This paper proposes a nonlinear approach for designing traffic responsive and coordinated ramp control using a self adapting fuzzy system. An adaptive neuro-fuzzy inference system (ANFIS) is used to incorporate a hybrid learning procedure into the control system. The traffic responsive metering rate is determined in every minute by the neuro-fuzzy control algorithm. Coordination between multiple on-ramps is ensured by the integration of a common input into all ramp controllers upstream of a bottleneck and a periodical update of the fuzzy control system in every 15 min. by a hybrid learning procedure. The objective of the online tuning process of the fuzzy parameters is to minimize the total time spent in the system. Therefore, Payne's traffic flow model and a deterministic queuing model are integrated into the control architecture To assess the impacts of the neuro-fuzzy ramp metering algorithm a section of 25 km of the A9 Autobahn was simulated with the FREQ model and compared with two other control scenarios. The results of the simulation of the neuro-fuzzy algorithm are very promising and an implementation of the neuro-fuzzy ramp metering system on a Munich middle ring road within the MOBINET project is planned.
Starting Page	94
Ending Page	99
File Size	551090
Page Count	6
File Format	PDF
ISBN	0780371941
DOI	10.1109/ITSC.2001.948636
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-08-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Traffic control Control systems Inference algorithms Fuzzy systems Nonlinear control systems Adaptive systems Programmable control Adaptive control Fuzzy control Roads
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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