Predicting Daily Tourist Flow in Scenic Areas Using LSTM and Big Data from Baidu Index

Abstract

The accurate prediction of tourist flow in scenic areas is crucial for effective tourist area management. This paper introduced the Baidu index associated with search engine data into relevant indicators that can be used to forecast daily tourist flow in scenic spots. The long short-term memory (LSTM) algorithm was employed for daily tourist flow prediction. Simulation experiments were conducted on the Longkou Nanshan Scenic Spot in Longkou City, Shandong Province, China. The experiment first verified the effectiveness of the feature indicators and then compared the predictive performance of the support vector machine (SVM), back-propagation neural network (BPNN), and LSTM models under the situation with or without the Baidu index. It was found that nine feature indicators exhibited significant correlations with daily flow, including date type, week type, month, number of holiday days, average tourist flow, antecedent daily flow, weather condition, standard deviation of daily flow, and Baidu index. The LSTM model demonstrated higher accuracy than SVM and traditional BPNN models. When using the same feature indicators, the p values between the LSTM model and the other two models were both 0.001, indicating significant differences. Furthermore, including the Baidu index as a feature significantly enhanced the accuracy of the prediction algorithm. When comparing within the same prediction algorithm, the p value between the algorithm using the Baidu index feature and the one without the Baidu index feature was less than 0.05, indicating a significant difference.