面向旅游突发事件的客流量混合预测方法研究

doi:10.16381/j.cnki.issn1003-207x.2017.05.020

中国管理科学 ›› 2017, Vol. 25 ›› Issue (5): 167-174.doi: 10.16381/j.cnki.issn1003-207x.2017.05.020

面向旅游突发事件的客流量混合预测方法研究

陈荣¹, 梁昌勇², 陆文星², 董骏峰², 葛立新³

1. 蚌埠学院经济与管理学院, 安徽蚌埠 233000;
2. 合肥工业大学管理学院, 安徽合肥 230009;
3. 蚌埠学院理学院, 安徽蚌埠 233000

收稿日期:2016-01-07 修回日期:2016-04-27 出版日期:2017-05-20 发布日期:2017-08-26
通讯作者: 梁昌勇(1965-),男(汉族),安徽肥西人,合肥工业大学管理学院教授,博士生导师,研究方向:管理信息系统、行为决策、信息管理与公共政策、云计算技术等,E-mail:cyliang@hfut.edu.cn. E-mail:cyliang@hfut.edu.cn
基金资助:
国家自然科学基金资助项目（71331002，71271072，71301037，71301040）；安徽高校自然科学研究重点项目（KJ2015A143）；安徽省教育厅2016年高校优秀拔尖人才培育资助重点项目（gxfxZD2016283）；蚌埠学院国家级项目培育基金项目

The Research of Tourist Flow Hybrid Forecasting Model for Tourism Emergency Events

CHEN Rong¹, LIANG Chang-yong², LU Wen-xing², DONG Jun-feng², GE Li-xin³

1. Department of Economic and Management, BengBu University, Bengbu 233000, China;
2. School of Management, HeFei University of Technology, Hefei 230009, China;
3. Department of Science, BengBu University, Bengbu 233000, China

Received:2016-01-07 Revised:2016-04-27 Online:2017-05-20 Published:2017-08-26

摘要/Abstract

摘要： 由于旅游突发事件的突然爆发性、危害性及信息不对称性，导致旅游客流量在短时间内发生急剧变化，原有模式被打破，非线性趋势和线性特征交织的随机性趋势明显，为旅游客流量正常预测带来极大的难度。本文提出一种面向旅游突发事件客流量混合预测方法，即支持向量回归（Support Vector Regression，SVR）和自回归求和移动平均模型（Autoregressive Integrated Moving Average，ARIMA）结合的混合预测方法：首先通过SVR预测旅游突发事件时期客流量，然后再用ARIMA预测SVR预测值的残差部分，最后将两者预测结果相加；同时针对客流量复杂特征，采用一种混沌粒子群算法（Chaos Particle swarm optimization，CPSO）实现对SVR参数选择。来自黄山风景区汶川地震时期客流量相关数据验证表明，混合预测模型优于单一预测方法，为旅游突发事件时期客流量预测提供了一种有效选择。

关键词: SVR, ARIMA, CPSO, 旅游突发事件, 客流量预测

Abstract: Because of sudden explosiveness and destructiveness as well as information asymmetry caused by tourism emergency events, the tourist flow deviates from original patterns and presents nonlinear and linear features, which causes a great difficulty to tourist flow forecasting. Traditional forecasting methods cannot solve this complicated problem. The article proposes a kind of tourist flow hybrid forecasting model for tourism emergency events which include two methods. One method is Support Vector Regression (SVR). It has good ability to deal with nonlinear and small sample problems and has been successfully used in many forecasting fields by researchers. The other method is Autoregressive Integrated Moving Average (ARIMA) which can deal with linear problem easily. At same time, the three parameters C,ε,σ of SVR affect the accuracy of forecast. A kind of Chaos Particle Swarm Optimization (CPSO) is used in the article. By the local search ability of Chaotic Local Search(CLS) as well as global search ability of Adaptive Inertia Weight Factor (AIWF) in CPSO, the optimal parameters C,ε,σ of SVR can be found effectively.
The detail process of tourist flow hybrid forecasting model is as follow. Firstly SVR is used to forecast tourist flow during emergencies. Meanwhile, CPSO is implemented to select the SVR parameters; Secondly ARIMA model is provided to forecast residual sequence of forecasting values. Finally two predicted values will be added, which leads to the final predicted values.
Data set from Mount Huangshan during Wenchuan Earthquakes period are used to validate the effectiveness of the hybrid models. The number of the data is from February 12, 2008 to June 12, 2008, including the daily tourist flow and daily tourist flow before eight o'clock. The results show that the hybrid approaches are significantly higher in accuracy than CPSO-SVR and PSO-SVR., which provide an effective choice to tourism emergency events flow forecasting as well as similar industries facing the same situation.Next researches will focus on tourist flow forecasting under the background of big data.

Key words: SVR, ARIMA, CPSO, tourism emergeney everts, tourist floco forecasting

中图分类号:

TP181

陈荣, 梁昌勇, 陆文星, 董骏峰, 葛立新. 面向旅游突发事件的客流量混合预测方法研究[J]. 中国管理科学, 2017, 25(5): 167-174.

CHEN Rong, LIANG Chang-yong, LU Wen-xing, DONG Jun-feng, GE Li-xin. The Research of Tourist Flow Hybrid Forecasting Model for Tourism Emergency Events[J]. Chinese Journal of Management Science, 2017, 25(5): 167-174.

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面向旅游突发事件的客流量混合预测方法研究

The Research of Tourist Flow Hybrid Forecasting Model for Tourism Emergency Events

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