Normalization methods on backpropagation for the estimation of driver's route choice
Year: 2005
Type: article
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.
DOI: 10.1007/bf02830631
Authors: Kyung Whan Kim, Daehyon Kim, Hun Young Jung
Institutions: Gyeongsang National University, Pusan National University
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $1,800
OpenAlex ID: W2021753381