Identifying traffic patterns of intersections accurately can provide technical support to optimize signal control schemes and realize intelligent control. The traffic patterns of intersections were defined from a qualitative point. Then, the identification parameters were reduced - based on the rough set theory - to search the minimum set of parameters used to characterize the feature of traffic patterns. Through combining fuzzy inference system and artificial network organically, a traffic pattern recognition model of intersections was constructed based on the fuzzy neural network, which has been used as a fuzzy recognizer in this paper. Finally, the paper simulated an intersection in Jinan by the simulation platform of VISSIM as the research object to analyze the recognition accuracy of the model comparatively. The results of experiments show that the model can recognize traffic patterns precisely, such as free traffic flow, steady traffic flow (tolerable delay), close to unstable traffic flow, unstable traffic flow and compulsory traffic flow.


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    Title :

    Traffic Pattern Recognition of Intersections Based on the Rough Fuzzy Neural Network


    Contributors:

    Conference:

    14th COTA International Conference of Transportation Professionals ; 2014 ; Changsha, China


    Published in:

    CICTP 2014 ; 689-702


    Publication date :

    2014-06-24




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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