Normalization methods on backpropagation for the estimation of driver's route choice

Abstract

The artificial neural network has recently been applied in many areas including transport engineering and planning. Even though its successful application for wide transportation areas, there are some major issues to be considered before using the neural network models, such as the network topology, learning parameter, and normalization methods for the input vectors. In this research, se veral normalization methods for input vectors were studied and the experimental results showed that the performance of the drier's ro ute choice model using the neural networks was dependent on the normalization methods. For the estimation of driver's route choice, the best normalization method in the Backpropagation neural network model was suggested in this study.