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基于节假日GRNN-PSO的旅游客流量 预测模型研究

发表时间:2018-04-28  浏览量:501  下载量:112
全部作者: 赵凤华,陈立秀
作者单位: 河北科技大学文法学院
摘 要: 旅游客流量的预测是旅游景区科学应对旅游高峰的关键,尤其是在节假日。提出广义回归神经网络-粒子群优化(general regression neural network-particle swarm optimization, GRNN-PSO)预测模型,针对2011年至2016年四川九寨沟景区节假日客流量的变化进行系统的实证研究。结果表明,与以往支持向量回归-粒子群优化(support vector regression-particle swarm optimization,SVR-PSO)、BP神经网络(back propagation neural network,BPNN)、自回归积分滑动平均(auto-regressive integrated moving average, ARIMA)模型等预测方法相比,构建的GRNN-PSO预测模型具有较高的精度和良好的普遍性、实用性。采用GRNN-PSO预测模型科学有效地预测旅游景区节假日客流量,可以准确掌握旅游景区在一定时期内旅游客流量的变化,从而为旅游景区合理应对节假日旅游提供科学的理论指导。
关 键 词: 科学学与科技管理;广义回归神经网络;粒子群优化;旅游客流量;数据预测
Title: The tourist flow forecasting based on GRNN-PSO model in holidays
Author: ZHAO Fenghua, CHEN Lixiu
Organization: School of Humanity and Law, Hebei University of Science and Technology
Abstract: The forecast of tourist flow is the key to scientifically deal with the tourism peak of tourist areas, especially in holidays. In this paper, the general regression neural network-particle swarm optimization (GRNN-PSO) model is proposed to make systematic and empirical study on the change of the holiday traffic in Jiuzhaigou scenic area in Sichuan province from 2011 to 2016. The study result shows that compared with the previous support vector reqression-particle swarm optimization (SVR-PSO), back propagation neural network (BPNN) and auto-regressive integrated moving average (ARIMA) model prediction methods, GRNN-PSO forecasting model established in this study has higher accuracy and better universality and practicality. GRNN-PSO model can scientifically and effectively forecast holiday traffic of tourist areas, and we can accurately grasp the traffic change of tourist area for a certain period in the future, which will provide a scientifically theoretical guidance for the tourist spots to deal with holidays.
Key words: science of science and technological management; general regression neural network; particle swarm optimization; tourist traffic; data forecasting
发表期数: 2018年4月第8期
引用格式: 赵凤华,陈立秀. 基于节假日GRNN-PSO的旅游客流量 预测模型研究[J]. 中国科技论文在线精品论文,2018,11(8):845-852.
 
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