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Tourism Demand Prediction Model Using Particle Swarm Algorithm and Neural Network in Big Data Environment.

Sai Xu¹, Shuxia Wang¹

¹School of Hospitality Administration, Zhejiang Yuexiu University, Shaoxing 312000, China.

Journal of Environmental and Public Health

|September 19, 2022

Summary

This study introduces a Particle Swarm Optimization-Neural Network (PSO-NN) model for more accurate tourism demand forecasting. The PSO-NN model significantly improves prediction accuracy compared to traditional Neural Network (NN) models.

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Area of Science:

Tourism Management
Artificial Intelligence
Forecasting Models

Background:

Accurate tourism demand forecasting is crucial for business operations.
Traditional Neural Network (NN) models face limitations like local optimization and slow convergence.

Purpose of the Study:

To develop an optimized tourism demand forecasting model.
To address the drawbacks of conventional NN models using Particle Swarm Optimization (PSO).

Main Methods:

Combining Particle Swarm Optimization (PSO) with Neural Network (NN) models.
Utilizing PSO to optimize NN weights and thresholds for enhanced prediction.
Analyzing tourism demand forecasting indexes, NN models, and implementation strategies.

Main Results:

The developed PSO-NN model achieved a prediction accuracy of 95.81%.
This represents a 10.09% improvement over conventional NN models.
Experimental findings validate the model's superior performance.

Conclusions:

The PSO-NN model offers a practical and effective solution for tourism demand forecasting.
This research demonstrates the feasibility and benefits of applying optimization models in tourism.
The findings have significant practical implications for the tourism industry.