To ease traffic congestion an inference system based on neural networks and fuzzy logic (neuro-fuzzy) has been developed and tested for traffic congestion at the junctions. Based on the input from the user, the neuro-fuzzy system developed is capable of taking the membership functions and rules of fuzzy on its own, thus making the system more user friendly and reliable. The parameters considered for traffic signal monitoring are density of the traffic, amount of distance between two vehicles, the delay encountered at the j unctions, traffic flow rate and length of the wait queue. The traffic flow is optimized by taking into account the average vehicle inflow rate and by considering the entry of vehicles in each separate lane as input.


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    Titel :

    Urban Traffic Density Decongestion Using Neural Networks and Fuzzy Logic Techniques


    Beteiligte:
    Samrudh, K (Autor:in) / Tusu, Satyam (Autor:in) / Shettar, Rajashree (Autor:in)


    Erscheinungsdatum :

    01.12.2017


    Format / Umfang :

    2744329 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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