基于网络搜索行为的世园会客流量预测预警研究

发布时间：2018-03-26 21:16

本文选题：客流量预测　切入点：世园会　出处：《青岛理工大学》2013年硕士论文

【摘要】：随着互联网的普及,旅游业的蓬勃发展,互联网技术广泛应用到旅游业中。目前,互联网作为重要的信息发布平台,许多旅游景区的相关人员利用互联网发布一些旅游信息,广大旅游者在出行前也通过互联网获取旅游信息。鉴于各国学者关于互联网技术的社会行为研究表明,互联网热词的网络搜索量与社会行为具有一定的联系,引导着实际的社会行为,其中包括旅游行为。为推进网络行为的研究,百度和谷歌分别推出了百度指数和谷歌趋势两大产品,为广大学者查询相关热词的网络搜索量提供了便利。本文通过运用定性、定量、实证研究等方法,证明热词的网络搜索量与世园会的实际客流量具有相关性。本文基于网络搜索行为构建世园会客流量预测和预警模型,通过借鉴有关网络行为方面的研究成果,构建世园会网络搜索热词的指标体系,利用谷歌趋势提供的网络搜索量,运用回归分析、灰色理论预测和预警世园会的客流量,并通过实证验证所构建模型具有实用性。本文主要研究内容包括以下六个方面： (1)阐述本文的研究背景和研究意义,分析国内外基于网络搜索行为的研究现状,提出本文的主要内容、研究框架、研究方法和技术路线。 (2)分析本文的相关理论,包括网络搜索行为和数据处理。首先介绍谷歌趋势的功能及应用方法,然后介绍灰色系统理论,包括原理、特点和主要内容。 (3)构建网络搜索热词的指标体系。根据指标体系构建的依据和原则,首先筛选出基准热词,然后通过热词推荐工具获取所有相关热词,最后依据热词的相关系数来确定是否选取。 (4)研究世园会客流量预测及预警的相关模型。本文利用回归理论构建世园会客流量的预测模型,同时利用灰色灾变理论构建世园会客流量的预警模型。 (5)以西安世园会为研究对象,通过西安世园会验证预测和预警模型的准确性,然后论述该模型亦可应用到青岛世园会。 (6)总结所做的工作,提出本文需要进一步改进的方向和对未来的展望。
[Abstract]:With the popularity of the Internet, the rapid development of the tourism industry, the Internet technology is widely applied to the tourism industry. At present, the Internet as an important platform for the dissemination of information, the relevant personnel of many scenic spots using the Internet to publish some tourist information, the majority of tourists before the trip through the Internet to get travel information. In view of social behavior of the scholars on the Internet the technology that has a certain network search volume and social behavior of Internet hot words, social behavior to guide practice, including tourism behavior. Research for the promotion of Internet behavior, Baidu and Google launched Baidu index and Google two products, related to the query hot words for the majority of scholars web search the amount of convenience.
In this paper, through the use of qualitative, quantitative, empirical research method, proved the relevance between the actual traffic network hot word search volume and World Horticultural Exposition. The network search behavior to build the world park passenger traffic forecasting and early warning model based on the research results based on network behavior, construct the index system of World Horticultural Exposition network hot word search, use Google provides Web searches, using regression analysis, grey theory of traffic forecasting and early warning in World Horticultural Exposition, and through the empirical validation of the model is practical. The main contents of this paper include the following six aspects:
(1) elaborate the background and significance of the research, analyze the research status of web search behavior both at home and abroad, and propose the main contents, research framework, research methods and technology roadmap.
(2) analyze the related theories in this paper, including web search behavior and data processing. First, introduce the functions and application methods of Google trend, and then introduce the grey system theory, including principles, characteristics and main contents.
(3) set up the index system of hot words. According to the basis and principles of index system construction, we first screened out the benchmark hot words, and then got all relevant hot words through the hot word recommendation tool, and finally determined the selection according to the correlation coefficient of the hot words.
(4) to study the relevant models of forecasting and early-warning of the world park's passenger volume. This paper applies regression theory to build the prediction model of the world park's passenger volume, and constructs the early-warning model of the world park's passenger volume based on the grey catastrophe theory.
(5) in Xi'an World Horticultural Exposition as the research object, the accuracy of forecasting and early warning model verification of Xi'an World Horticultural Exposition, and then discusses the model can also be applied to the Qingdao World Horticultural Exposition.
(6) summing up the work done and putting forward the direction for further improvement and prospects for the future.

【学位授予单位】：青岛理工大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TP391.3;F591

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