• 论文 •

### 基于改进PSO-BP神经网络的旅游客流量预测方法

1. 1. 合肥工业大学管理学院,   合肥  230009; 2. 智能决策与信息系统技术教育部工程研究中心, 合肥 230009
• 出版日期:2020-08-25 发布日期:2020-09-24

LU Wenxing, DAI Yiru, LI Chu, LI Keqin. Tourist Traffic Flow Forecasting Method Based on Improved PSO-BP Neural Network[J]. Journal of Systems Science and Mathematical Sciences, 2020, 40(8): 1407-1419.

### Tourist Traffic Flow Forecasting Method Based on Improved PSO-BP Neural Network

LU Wenxing 1,2 ,DAI Yiru 1,2 ,LI Chu 1,2, LI Keqing 1,2

1. 1. School of Management, Hefei University of Technology, Hefei 230009; 2. Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information Systems Technologies, Hefei 230009
• Online:2020-08-25 Published:2020-09-24

Improving the forecasting accuracy of daily passenger traffic in scenic spots is significant to the daily operation management of scenic spots and the conservation of tourism resources. PSO-BP has been applied widely to the prediction. Based on the fact that the inertia weight of PSO algorithm cannot meet the requirements of particle diversity and local extremum when it changes linearly, this paper proposes to use the fitness value of particles to change the inertia weight dynamically and nonlinearly. In this paper, the particle fitness value is used to change the inertia weight by the improved method of PSO-BP. Meanwhile, the particle swarm optimization algorithm is improved by adding position perturbation in the iteration period of particles and combining with the particle iteration period to increase the position disturbance. The improved PAPSO (particle swarm optimization algorithms with position disturbance and adaptive inertia weight, PAPSO) is used to optimize the initial weights and thresholds of BP neural network. The researchers established a Matlab prediction model of the daily passenger flow in Huangshan scenic area and conducted an experiment on the tourist flow data of Huangshan. The results show that the prediction model based on PAPSO algorithm to optimize BP neural network improves the prediction accuracy effectively.

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