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Mathematical Problems in Engineering
Volume 2014, Article ID 457197, 10 pages
Research Article

Real-Time Forecast of Tourists Distribution Based on the Improved k-Means Method

Business School, Sichuan University, Chengdu 610064, China

Received 29 March 2014; Revised 1 May 2014; Accepted 15 May 2014; Published 5 June 2014

Academic Editor: Ker-Wei Yu

Copyright © 2014 Peiyu Ren et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Tourist distribution, a vector to reflect the tourist number of every scenic spot in a certain period of time, serves as the foundation for a scenic spots manager to make a schedule scheme. In this paper, a forecast model is offered to forecast tourist distribution. First of all, based on the analysis of changing mechanism of tourist distribution, it is believed that the possibility for a scenic spot to have the same tourist distribution next time is high. To conduct this forecast, we just need to research on the similar tourist distributions of which time and tourist scale are close. Considering that it is time-consuming, an improved K-means cluster method is put forward to classify the historical data into several clusters so that little time will be needed to search for the most similar historical data. In the end, the case study of Jiuzhai Valley is adopted to illustrate the effectiveness of this forecast model.