NDLI: Intelligent model system for tourism flow prediction: a study of Xi'an Museum

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Intelligent model system for tourism flow prediction: a study of Xi'an Museum

Content Provider	ACM Digital Library
Author	Qian, Feng Han, Cao Haiyan, Meng
Abstract	Tourism flow is influenced by many factors, so accurate tourism flow forecasting is a most important and difficult issue in tourism industry. Here a new intelligent model system for tourism flow prediction is proposed which combine statistical methods with BPNN model (SMBPS). SMBPS is divided into three stages. The first stage is data analysis and preprocessing. Official holiday is seen as emergency event, trend extrapolation is used to smooth the tourist flow of emergency event which fluctuate remarkably, the formula of the human body comfort is used to acquire the comfort-index level and Monthly index is used to divide a year into four tourist seasons; The second stage, four BPNN models are built for four tourist seasons respectively in order to predict the tourist flow in normal working days as different tourist season has different characteristics; in the last stage, the fluctuation coefficient is used to predict the tourist flow of emergency event dynamically. The experimental results indicate that the SMBPS is an effective approach which is more accurate than any other models and can be used as a suitable forecasting tool to solve the problem of scenic spot forecasting.
Starting Page	1
Ending Page	7
Page Count	7
File Format	PDF
ISBN	9781450347990
DOI	10.1145/3028842.3028845
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-12-23
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Bpnn Dynamic Tourist flow Statistics method Smbps
Content Type	Text
Resource Type	Article

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