Research on the Forecast Model of Tourist Flow in Scenic Spots Based on Big Data Analysis

Abstract

The forecasting accuracy of tourist flow in scenic spots directly affects the management level of scenic spots. The modeling efficiency of the current tourist flow forecasting models in scenic spots is poor and it takes a long time. In order to obtain the ideal tourist flow forecast results in scenic spots, the design is based on the characteristics of changes in tourist flow in scenic spots A forecasting model of tourist flow in scenic spots based on big data analysis. First, analyze the current tourist flow forecasting progress of the scenic spot, find out the various shortcomings of the tourist flow forecast of the scenic spot, and then collect the passenger flow data of the scenic spot for a period of time, and use the ARIMA model and the BP neural network to analyze the seasonality and randomness of the tourist flow of the scenic spot. The change characteristics are modeled, and their prediction results are weighted to obtain the tourist flow forecast results of the scenic spot. Finally, the comparison test is carried out with the current classic scenic spot passenger flow prediction model. Compared with the classic model, the big data analysis of the tourist flow fitting of the scenic spot The accuracy of forecasting and forecasting has been improved to a certain extent. At the same time, the modeling efficiency of tourist flow in scenic spots has been improved, which can provide valuable information for scenic spot managers, thereby improving the management level of tourist flow in scenic spots.