Real-time forecast of tourists distribution based on the improved k-means method

Abstract

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. © 2014 Peiyu Ren et al.