地理科学进展 ›› 2012, Vol. 31 ›› Issue (10): 1353-1359.doi: 10.11820/dlkxjz.2012.10.013

• 旅游与社会地理 • 上一篇    下一篇

基于EMD的景区客流波动特征及其组合预测——以黄山风景区为例

余向洋1, 沙润2, 朱国兴1, 胡善风1   

  1. 1. 黄山学院旅游学院, 黄山245021;
    2. 南京师范大学地理科学学院, 南京210097
  • 收稿日期:2011-11-01 修回日期:2012-02-01 出版日期:2012-10-25 发布日期:2012-10-25
  • 作者简介:余向洋(1969-),男,安徽岳西人,博士,副教授,研究方向为旅游规划与管理。E-mail:yxy417@126.com
  • 基金资助:

    国家自然科学基金项目(41071327);安徽省教育厅人文社科重点项目(SK2012A118);安徽省高校科学研究项目(KJ2011Z366);黄山学院科研项目(2011xkjq001)。

Research on Fluctuation Characteristics and Combined Forecasting of Tourist Arrivals in Huangshan Scenic Areas

YU Xiangyang1, SHA Run2, ZHU Guoxing1, HU Shanfeng1   

  1. 1. Department of Tourism, Huangshan College, Huangshan 245021, China;
    2. School of Geographical Science, Nanjing Normal University, Nanjing 210097, China
  • Received:2011-11-01 Revised:2012-02-01 Online:2012-10-25 Published:2012-10-25

摘要: 旅游地的发展演化过程研究大多采用Bulter 的生命周期理论路径, 少有文献从波动的视角理解和分析旅游地的发展演化过程。本文以黄山风景区为例, 采用经验模态分解方法(EMD)尝试从波动的视角分析景区客流波动特征, 并利用波动性特征对其发展进行组合预测(经验模态分解方法和最小二乘支持向量机方法的组合)。研究结果表明:黄山景区客流波动呈现出多种形态, 在增长趋势的基础上叠加了季节性波动、景区旅游周期波动和景区经济周期波动。其与最小二乘支持向量机组合预测模型能够对景区客流进行有效预测, 并且运算速度快, 预测精度有所提高;与生命周期曲线相比较更加直观、微观、准确, 并且能够进行较为准确的客流预报, 有助于景区规划管理和战略决策。

关键词: 黄山风景区, 经验模态分解, 客流波动, 组合预测, 最小二乘支持向量机

Abstract: The research on dynamic evolution of tourist destination has been confined to the path of Bulter’s destination lifecycle model so that other research perspectives including fluctuation model have been neglected. Taking Huangshan Scenic Areas as a case study, this paper analyzes fluctuation characteristics of tourist arrivals by Empirical Mode Decomposition (EMD), and employs a combined forecasting model to predict tourist arrivals based on EMD and LS-SVM (Least Squares Support Vector Machines). The results show that the fluctuationof tourist arrials in Huangshan Scenic Areas present such patterns as continuously increasing trend, seasonal fluctuation, tourism cycles and economic cycles, and the combined forecasting model can predict tourist arrivals more rapidly and more accurately. All in all, EMD from fluctuation perspective can disclose dynamic evolution more directly, deeply and accurately, and combined with LS-SVM it can accurately predict tourist arrivals, which is conductive to planning management and strategic decision of scenic areas.

Key words: combined forecasting, Empirical Mode Decomposition (EMD), fluctuation of tourist arrivals, Huangshan Scenic Areas, Least Squares Support Vector Machines (LS-SVM)