A Tourist Flow Prediction Model for Scenic Areas Based on Particle Swarm Optimization of Neural Network

Abstract

In recent years, China has been expanding domestic demand and promoting the service industry. This is a mixed blessing for the further development of tourism. To make accurate prediction of tourist flow, this paper proposes a tourist flow prediction model for scenic areas based on the particle swarm optimization (PSO) of neural network (NN). Firstly, a system of influencing factors was constructed for the tourist flow in scenic areas, and the factors with low relevance were eliminated through grey correlation analysis (GCA). Next, the long short-term memory (LSTM) NN was optimized with adaptive PSO, and used to establish the tourist flow prediction model for scenic areas. After that, the workflow of the proposed model was introduced in details. Experimental results show that the proposed model can effectively predict the tourist flow in scenic areas, and provide a desirable prediction tool for other fields.