基于互联网旅游数据的游客量预测模型研究现状与展望被引量：2

Review and Prospect of Tourist Volume Prediction Models Research Based on Internet Tourism Data

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摘要互联网旅游数据具有多元异构、高频、海量、价值密度低的大数据特征,从互联网旅游数据中挖掘关键特征信息和构建有效的游客量预测模型已成为近年来国内外相关科研机构的研究共识和热点。对近十年国内外基于互联网旅游数据的游客量预测模型研究现状进行综述。首先,介绍了互联网旅游数据的特点和来源,阐述了搜索引擎数据和社交媒体数据的获取过程和处理方法;其次,对基于互联网旅游数据的游客量预测模型现状进行评述,包括时间序列预测模型、计量经济预测模型、机器学习预测模型和组合预测模型;最后,从关键词智能提取、非结构化数据转化、多源旅游数据融合、高维非线性混频数据处理4个方面展望了未来的研究要点及趋势。 Intemet tourism data had the characteristics of multiple heterogeneous,high frequency,mass and low value density.Mining key feature information from Interent tourism data and constructing effective tourist volume prediction models had become the research consensus and hot spot of relevant research institutions domestic and abroad.This paper reviewed the research status of tourist volume prediction models based on internet data domestic and abroad in recent ten years.Firstly,this paper introduced the characteristics and sources of internet tourism data,and expounded the acquisition process and processing methods of search engine data and social media data.Secondly,this paper summarized tourism volume prediction models based on internet tourism data,including time series prediction model,econometric prediction model,machine learning prediction model and combination prediction model.Finally,this paper proposed the four aspects including intelligent keyword extraction,unstructured data transformation method,integration of Internet multi-source big data,high-dimensional nonlinear mixing data method should be the future research points and trends.

作者时萍萍胡姚刚孟继东 SHI Ping-ping;HU Yao-gang;MENG Ji-dong(Chongqing University of Technology School of Management,Chongqing 400054,China;Chongqing University of Technology School of Electrical and Electronic Engineering,Chongqing 400054,China;Chongqing Tourism Talent Development Research Institute,Chongqing University,Chongqing 400044,China;Postdoctoral Workstation,Bank of Chongqing,Chongqing 400024,China)

机构地区重庆理工大学管理学院重庆理工大学电气与电子工程学院重庆大学重庆旅游人才发展研究院重庆银行博士后科研工作站

出处《资源开发与市场》 CAS 北大核心 2022年第8期921-929,共9页 Resource Development & Market

基金重庆市教委人文社会科学研究项目(编号:20SKGH170) 重庆理工大学科研启动基金(编号:2019ZD30)。

关键词互联网旅游数据游客量搜索引擎社交媒体预测模型 Internet tourism data tourist volume search engines social media prediction model

分类号 F590 [经济管理—旅游管理] F224 [经济管理—产业经济]