Vehicle reidentification is the process of tracking a vehicle along a highway as it crosses detection stations. Inductive loop detectors are by far the most widely deployed vehicle detectors. In the present work, vehicle reidentification is performed by combining vehicle-specific information (length and electromagnetic signatures) and some contextual information (lane, speed, and time) to form a decision tree. This approach provides a specific decision tree for tracking vehicles along each highway section. After training, the decision tree successfully classified about 95% of the unseen test records—a significant improvement relative to the literature and our own previous work on the same data. This success rate has been consistently obtained from two data sets: one consisting only of passenger vehicles and another consisting of a representative traffic mix.


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    Title :

    Using Decision Trees to Improve the Accuracy of Vehicle Signature Reidentification


    Additional title:

    Transportation Research Record


    Contributors:


    Publication date :

    2004-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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