The Exploratory Study About The Current Situation Of Package Tour

In recent years, tourism has developed rapidly. The number of global tourists in 2015 set a new record, reaching 1.184 billion. In 2015, the number of tourists at home and abroad that China received is more than 4.1 billion, and total tourism income exceeds 4 trillion yuan, which is an increase of 10% and 12% respectively, compared with 2014. While the rapid development of tourism, some changes have taken place in tourism way. Self-help and self-driving tour persists overheating, and private custom and other new products are very popular with the market. In that way, what is the current situation of the traditional package tour? Is the number of orders rising or falling? In addition, what’s the characteristic of the package tour orders and so on, these issues are worthy of study, and package tour is one of the most concern of the tourist companies.In this paper, we select all order data of the surrounding package tour project in an OTA, and explore the characteristics of package tour from two aspects. First, we use PAM clustering algorithm to cluster the sample orders, and the sample orders can be divided into four clusters, each cluster has different characteristics, and in accordance with the actual situation. The second aspect, we use three models to fit the series of daily successful orders, which are seasonal ARIMA model, AR(2) model based on the analysis of deterministic factor, and the ARCH(2) model(the delay variables as regression factor) based on the analysis of deterministic factor. With more information we consider, the model fitting effect gets better and better. However, from the effect of 6 period forecast, the first model--seasonal ARIMA model is better. In practical application, the project staff can take advantage of the excavated clustering results and the predicted results of orders to develop relevant strategy, thus to promote the operation effect of the product.