Accurate vehicle classification and traffic composition data are an important traffic performance measures which are used in many transportation applications. In this paper, an attempt is made to develop a model to classify the vehicles into five categories: light commercial vehicle, car/jeep/van, two-axle truck/bus, three-axle truck, and multi-axle vehicles based on the fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) algorithms. Wheelbase and average track of the vehicles are the two parameters considered in the classification of the vehicles which can be measured easily in the field. Two wheelers and three wheelers are not considered in this study as these can be easily classified based on the selected parameters. Both of these parameters of all classified vehicles on Indian roads have been collected from various sources including the Web sites of automobile companies. Vehicle classification has been done for decided classes of vehicles to check the accuracy and performance of the model. Model with modified FIS by ANFIS is more significant than a model with the initial FIS. The overall classification rate is significant with 89.34 % accuracy.


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    Title :

    Vehicle Classification Using Adaptive Neuro-Fuzzy Inference System (ANFIS)


    Additional title:

    Advs in Intelligent Syst., Computing




    Publication date :

    2014-12-24


    Size :

    16 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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