<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The accurate prediction of visitor flow in tourist attractions presents a significant challenge within the tourism industry and holds substantial reference value for both park management and tourist experiences. Addressing this, our study develops a predictive model specifically tailored to tourist sites using trajectory data. Recognizing the limitations of current algorithms in identifying accurate stay regions, we utilize a segmentation method predicated on change points. This approach integrates a Back Propagation (BP) neural network with the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm to enhance the precision of stay region identification. Building upon this foundation, we further incorporate Gaussian fitting techniques to construct a comprehensive crowd prediction model for tourist attractions. The research results verify that the model in this paper can estimate the passenger flow better by predicting the passenger flow of Zhongshan Park in city A. It is found that when the passenger flow is below 15000, the passenger flow is less. When the passenger flow is larger in the range of 15000~30000, and when the passenger flow is more than 30000, it will be saturated and crowded, and the model constructed in this paper has a more accurate passenger flow. The model built in this paper has a high accuracy of people flow prediction value.</p>
</abstract>

The accurate prediction of visitor flow in tourist attractions presents a significant challenge within the tourism industry and holds substantial reference value for both park management and tourist experiences. Addressing this, our study develops a predictive model specifically tailored to tourist sites using trajectory data. Recognizing the limitations of current algorithms in identifying accurate stay regions, we utilize a segmentation method predicated on change points. This approach integrates a Back Propagation (BP) neural network with the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm to enhance the precision of stay region identification. Building upon this foundation, we further incorporate Gaussian fitting techniques to construct a comprehensive crowd prediction model for tourist attractions. The research results verify that the model in this paper can estimate the passenger flow better by predicting the passenger flow of Zhongshan Park in city A. It is found that when the passenger flow is below 15000, the passenger flow is less. When the passenger flow is larger in the range of 15000~30000, and when the passenger flow is more than 30000, it will be saturated and crowded, and the model constructed in this paper has a more accurate passenger flow. The model built in this paper has a high accuracy of people flow prediction value.

Construction of a forecasting model for tourist attraction footfall

Qinhuangdao Vocational and Technical College

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Construction of a forecasting model for tourist attraction footfall"}

The accurate prediction of visitor flow in tourist attractions presents a significant challenge within the tourism industry and holds substantial reference...

Construction of a forecasting model for tourist attraction footfall

Publié en ligne: 05 juil. 2024

Pages: -

Reçu: 21 mars 2024

Accepté: 09 juin 2024

DOI: https://doi.org/10.2478/amns-2024-1592

Mots clés
Footfall prediction model, Trajectory data, BP neural network algorithm, DBSCAN density clustering algorithm

© 2024 Jianfeng Cui et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Construction of a forecasting model for tourist attraction footfall

Publié en ligne: 05 juil. 2024

Pages: -

Reçu: 21 mars 2024

Accepté: 09 juin 2024

DOI: https://doi.org/10.2478/amns-2024-1592

Mots clésFootfall prediction model, Trajectory data, BP neural network algorithm, DBSCAN density clustering algorithm

© 2024 Jianfeng Cui et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Footfall prediction model, Trajectory data, BP neural network algorithm, DBSCAN density clustering algorithm