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.


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    Title :

    A neuro-fuzzy algorithm for coordinated traffic responsive ramp metering


    Contributors:


    Publication date :

    2001


    Size :

    6 Seiten, 8 Quellen




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    A neuro-fuzzy algorithm for coordinated traffic responsive ramp metering

    Bogenberger, K. / Keller, H. / Vukanovic, S. | IEEE | 2001


    A Neuro-Fuzzy Algorithm for Coordinated, Traffic-Responsive Ramp Metering

    Bogenberger, K. / Keller, H. / Vukanovic, S. et al. | British Library Conference Proceedings | 2001




    ACCEZZ - Adaptive fuzzy algorithms for traffic responsive and coordinated ramp metering

    Bogenberger, K. / Vukanovic, S. / Keller, H. | Tema Archive | 2002