The Study Of World Expo Passenger Flow Forecast And Early Warning Based On WEB Search Behavior

As the popularity of the Internet, and the flourishing of the tourism, the Internet technology has been widely applied to the tourism industry. Currently, the Internet as an important platform for the dissemination of information has been used by many relevant staff of tourist attractions for publishing tourist information, while the majority of tourists also get tourist information via the Internet before the trip. The scholars’ research on social behavior of Internet technology shows that there has some links between the search volume of Internet hot words and social behavior. This Network information guides the actual social behavior and tourism also belongs to this kind of social behavior. In order to promote the network behavior research, Baidu and Google companies respectively launched Baidu Index and Google Trends. They provide a convenient for the majority of scholars to query the web search volume of related hot words.This paper proves that web search volume is closely associated with the actual passenger flow of the World Horticultural Exposition with using the method of qualitative, quantitative and empirical study. By referring to network behavioral research, this paper builds an index system about the web search hot words of Horticultural and constructs the World Expo passenger traffic forecasting and early warning model which based on web search behavior with making full use of Web search volume provided by Google trends, multiple regression theory and gray theory. Finally, through the empirical to verify that the model is practical. The paper mainly includes six aspects as follows:(1) Describe the research background and significance of this paper, analyze the behavior of domestic and foreign on Web search research, and present the main contents, research framework, research methods and technology roadmap.(2) Analyze theory and related technology, including Web search behavior and data processing. First introduces the function and application method of Google Trends, and then describes the method of gray theory, including its principle, characteristics and main contents.(3) Construct the hot word index system. According to the principles of the index system, first select the benchmark hot words, and then through the hot words recommended tools get all relevant hot words, at last determine whether the hot words should be chose on the basis of the correlation coefficient.(4) Research forecasting and early warning related models on Horticultural traffic. In this paper, multivariate regression theory is used to build prediction model, while taking advantage of the gray catastrophe theory constructs warning model.(5) Take the World Horticultural Exposition in Xi’an as the research object; verify the accuracy of forecasting and early warning model by Xi’an International Horticultural Exposition, and then discuss the model can also be applied to the Qingdao International Horticultural Exposition.(6) Summarize the work that have been done in this paper, and put forward the improve direction and vision for the future.