| 基于APSO—SVR的山岳风景区短期客流量预测 |
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| 引用本文： | 陈荣,梁昌勇,梁焱,马银超.基于APSO—SVR的山岳风景区短期客流量预测[J].旅游科学,2013,27(3):50-60. |
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| 作者姓名： | 陈荣 梁昌勇 梁焱 马银超 |
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| 作者单位： | 合肥工业大学管理学院,安徽合肥,230009 |
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| 基金项目： | 国家自然科学基金面上项目"基于行为决策的隐性目标决策模型与方法研究"，安徽高校省级自然科学研究项目"风景名胜区客流量预测模型与对比方法研究"，安徽省科技计划项目"智慧黄山风景区人流量智能分析预测系统" |
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| 摘 要： | 根据山岳风景区短期客流量小样本、非线性等特征，本文提出基于自适应粒子群算法（Adaptive Particle Swarm Optimization，APSO）的支持向量回归（Support Vector Regression，SVR）模型，融合SVR处理小样本、非线性预测特性和APSO优化SVR参数的能力对山岳风景区短期客流量进行预测。来自山岳风景区黄山2008年～2011年暑期相关日数据的验证结果表明：与PSO—SVR、GA—SVR和BPNN等模型相比，APSO。SVR模型的预测准确性更高、误差更小，是进行山岳风景区短期客流量预测的有效工具。
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| 关 键 词： | 短期客流量 山岳风景区 支持向量回归 自适应粒子群算法 预测模型 |
Forecasting Short-Term Tourism Flow of Mountain Resorts Based on Adaptive PSO-SVR |
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| CHEN Rong
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LIANG Changyong
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LING Yan
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MA Yinchao.Forecasting Short-Term Tourism Flow of Mountain Resorts Based on Adaptive PSO-SVR[J].Tourism Science,2013,27(3):50-60. |
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| Authors: | CHEN Rong LIANG Changyong LING Yan MA Yinchao |
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| Institution: | ( School of Management, HefFei University of Technology, tteFei 230009, China) |
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| Abstract: | According to small samples and nonlinear characteristic of mountain resorts, the article combines Support Vector Regression with Adaptive Particle Swarm Optimization, which uses superiority of SVR in small samples and nonlinear forecasting and APSO searching for SVR model parameters of optimization, to forecast short-term tourism flow. The daily data set of a 5A mountain resorts from 2008 to 2011 summer holidays in Mount Huangshan is applied as an example. The experimental results demonstrate that the APSO-SVR approach is an effective way to forecast short-term tourism flow with greater accuracy and few errors of all above models including those of PSO-SVR, GA-SVR and BPNN. |
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| Keywords: | short-term tourism flow mountain resorts SVR APSO forecasting model |
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