Forecasting Smart Tourism Visitor Flows Leveraging Big Data Technology Assistance

This study aims to explore the forecasting effect of smart tourism passenger flow supported by big data technology and improve the intelligence of smart tourism. In view of the differences in tourist traffic due to different times, the tourist traffic data in Xi'an from May 1, 2020, to April 1, 2021 are used as the sample period. Autoregressive Integrated Moving Average (ARIMA) is used to build a smart model of the tourism passenger flow prediction. The predictive performance of the constructed model is evaluated and analyzed. The results show that the prediction errors of the model algorithm Root Mean Squared Error (RMSE) and Mean Squared Error (MSE) are 2.22×10^1 and 4.95×10^2, respectively, which are smaller than other algorithms. The error is compared with the actual passenger flow with the highest accuracy. Therefore, the constructed model has high prediction accuracy in predicting and analyzing smart tourism passenger flow, which can provide a reference for the later tourist management and intelligent development of scenic spots.