INDUCTIVE CLASSIFYING ARTIFICIAL NETWORK FOR VEHICLE TYPE CATEGORIZATION

As transportation surveillance technology continues to advance, the measurement of more complete traffic information is becoming increasingly feasible. ICAN stands for Inductive Classifying Artificial Network and is used to conveniently describe a self- organizing feature map (SOFM) for vehicle type categorization using inductive signatures as input. Vehicle type categorization is the separation of vehicles into predefined classes and can be useful for improving transportation efficiency, cost, environmental sustainability, enforcement, safety, and education. ICAN mainly focuses on the challenging task of differentiating between two-axle vehicles such as passenger car, sports utility vehicle (SUV), van truck, and bus. This is in contrast to systems that classify according to the number of axles. One characteristic of ICAN is the simplicity of the 13 neuron 1-dimensional neural network, and the employment of a small training set of 13 signatures. The overall classification results of 87% (dataset 1) and 82% (dataset 2) for 7 categories coupled with consistent performance across all vehicle categories was significant and encouraging