Vehicle classification is the process of separating vehicles according to various predefined classes. Vehicle-classification information can be used in many transportation applications, including road maintenance, emissions/pollution estimation, traffic modeling and simulation, traffic safety, and toll setting. An example of a classification scheme using the following seven vehicle classes is presented: cars, sport-utility vehicles/ pickups, vans, limousines, buses, two-axle trucks, and trucks with more than two axles. This system uses vehicle inductive signatures collected from existing loop-detector infrastructure. It also uses a heuristic-discriminant algorithm for classification and a multi-objective optimization for training the heuristic algorithm. Feature vectors obtained by processing inductive signatures are used as inputs into the classification algorithm. Three different heuristic algorithms were developed, yielding encouraging results of 81 to 91 percent overall classification rates. The results demonstrate the potential of collecting network-wide vehicle-classification data from inductive loops. The availability of vehicle-classification data helps to improve traffic surveillance and better defines dynamic traffic networks.


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    Title :

    Heuristic Vehicle Classification Using Inductive Signatures on Freeways


    Additional title:

    Transportation Research Record


    Contributors:


    Publication date :

    2000-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Heuristic Vehicle Classification Using Inductive Signatures on Freeways

    Sun, C. / Ritchie, S. G. / National Research Council | British Library Conference Proceedings | 2000


    Freeways

    Halprin, Lawrence | TIBKAT | 1966


    Freeways

    Elefteriadou, Lily | Springer Verlag | 2013


    Wavelet–k Nearest Neighbor Vehicle Classification Approach with Inductive Loop Signatures

    Jeng, Shin-Ting (Cindy) / Chu, Lianyu / Hernandez, Sarah | Transportation Research Record | 2013