EHS
EHS

Design of Machine Learning Algorithm for Tourism Demand Prediction



. 2022 Jun 8;2022:6352381.


doi: 10.1155/2022/6352381.


eCollection 2022.

Affiliations

Item in Clipboard

Nan Yu et al.


Comput Math Methods Med.


.

Abstract

Unused hotel rooms, unused event tickets, and unsold items are all examples of wasted expenses and earnings. Governments require accurate tourism demand forecasting in order to make informed decisions on topics such as infrastructure development and lodging site planning; therefore, accurate tourism demand forecasting becomes vital. Artificial intelligence (AI) models such as neural networks and security violation report (SVR) have been used effectively in tourist demand forecasting as a result of the fast advancement of AI. This paper constructs a tourism demand forecasting model based on machine learning on the basis of the existing forecasting model research. The completed work is as follows: (1) It introduces a large number of domestic and foreign literatures on tourism volume forecasting and proposes the research content of this paper. (2) It is proposed to stack the long short-term memory- (LSTM-) based autoencoders deeply, by adopting a hierarchical greedy pretraining method to replace the random weight initialization method used in the deep network and combining this pretraining stage and fine-tuning network together to form the SAE-LSTM prediction model for improving the performance of deep learning models. (3) This paper uses the monthly search engine strength data of city A’s monthly tourist volume and its related influencing factors as the data set; processes the data set to make the model adapt to the data input; uses mean absolute error (MAE), root mean square error (RMSE), MAPE, and other model evaluation indicators; and uses LSTM and the constructed SAE-LSTM model to conduct comparative experiments to predict the number of tourist arrivals in four years. The prediction results of the models proposed in this paper are better than those of the LSTM model. According to the experimental results, the superiority of the proposed LSTM-based unsupervised pretraining method is demonstrated.

Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures



Figure 1

LSTM model.


Figure 2


Figure 2

Autoencoder structure.


Figure 3


Figure 3

LSTM-based autoencoder pretrained model.


Figure 4


Figure 4

MAPE comparison of the two models.


Figure 5


Figure 5

MAE comparison of the two models.


Figure 6


Figure 6

RMSE comparison of the two models.

References

    1. Hu Y. C. Developing grey prediction with Fourier series using genetic algorithms for tourism demand forecasting. Quality & Quantity . 2021;55(1):315–331. doi: 10.1007/s11135-020-01006-5.



      DOI

    1. Sun N., Ma J. Forecast on the tourist-generating market of Beijing Olympic Games in 2008 based on tourism background trend line. Geographical Research . 2008;27(1):65–74.

    1. Su P. Study on Scenic Area Tourism Passenger Flow Short-Term Forecast Method, [Ph.D. thesis] Hefei University of Technology; 2013.

    1. Vu C. Effect of demand volume on forecasting accuracy. Tourism Economics . 2006;12(2):263–276. doi: 10.5367/000000006777637412.



      DOI

    1. Bi W., Liu Y., Li H. Daily tourism volume forecasting for tourist attractions. Annals of Tourism Research . 2020;83, article 102923 doi: 10.1016/j.annals.2020.102923.



      DOI



      PMC



      PubMed



Source link

EHS
Back to top button