FORECAST OF TOURISM FLOW VOLUME OF TOURISTATTRACTION BASED ON DEGREE OF TOURISTATTENTION OF TRAVEL NETWORK: A CASE STUDY OF BAIDU INDEX OF DIFFERENT CLIENTS
SUN Ye, ZHANG Hong-lei, LIU Pei-xue, ZHANG Jie
School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China
Abstract It is the web search engine that is an important way for tourists to get travel information. Therefore, it is easy to record behavior of tourists. Baidu Index, by searching times of relevant keywords, is able to find out the degree of tourist attention of travel network easily. With the changes of different clients, Baidu Index shows certain spatiotemporal difference and precursor effect. In order to find out the relationships between Baidu Index of PC client and mobile client and the actual visitor number of Mount Sanqingshan, paper used the econometric cointegration theory and Granger causality test. In addition, to forecast Tourism Flow Volume, the paper further establishes ARMA model of the daily visitor number of Mount Sanqingshan and VAR models which add Baidu Index of PC client or mobile client respectively. It is found that:1) There are long-term equilibrium relationships between the actual visitor number of Mount Sanqingshan and Baidu Index of PC client and mobile client of multigroup search keywords; 2) The results of variables Granger causality tests between Baidu Index of PC client and mobile client of Different search keywords and the actual visitor number of Mount Sanqingshan present significant inconsistencies; 3) Among the three prediction model, VAR model of mobile client is of the best prediction accuracy and ARMA model of the daily visitor number of Mount Sanqingshan is of the lowest prediction accuracy.
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2017, Vol. 32 
