Abstract
A new prediction algorithm of tourists flow based on clustering-based generalized regression neural network (GRNN) is proposed in this paper. In order to analyze tourists’ behavior, we use the clustering-based GRNN method to estimate the entering rate of each pavilion at Zone D of Shanghai Expo site. The extensive experimental results show that the proposed algorithm exceeds other prediction methods of neural network like back propagation (BP) method on efficiency and correctness.
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© 2012 Springer-Verlag Berlin Heidelberg
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Hu, Y., Xie, R., Zhang, W. (2012). Tourists Flow Prediction by Clustering-Based GRNN. In: Zhang, W., Yang, X., Xu, Z., An, P., Liu, Q., Lu, Y. (eds) Advances on Digital Television and Wireless Multimedia Communications. Communications in Computer and Information Science, vol 331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34595-1_54
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DOI: https://doi.org/10.1007/978-3-642-34595-1_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34594-4
Online ISBN: 978-3-642-34595-1
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