Scenic Spot Tourists Flow Prediction Research Based On Web Search Items

DOI

10.2991/jimec-17.2017.100How to use a DOI?

Keywords

BigData; Search Index; Support Vector Machines; Data Test; Regression.

Abstract

In the context of "smart tourism" for large data applications, the Internet search engine records a large number of people searching for data. Compared with the official statistics, the search data has the characteristics of high efficiency and low cost. In this paper, we will explore and analyze the relationship between network search terms and scenic tourist numbers, analyze the theory of support vector machine in time series forecasting, and propose a support vector machine (SVM) algorithm for the forecast of scenic area passenger flow. The forecasting of the tourist flow rate of the local tourist area is carried out by the method of controlling the number of support vectors to reduce the calculation amount of the algorithm. Finally, the results show that the model has good prediction precision and can be used to predict the tourist attractions' scenic passenger flow.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Fu Tian
AU  - Wang Zhen
AU  - Xun Song Ming
PY  - 2017/10
DA  - 2017/10
TI  - Scenic Spot Tourists Flow Prediction Research Based On Web Search Items
BT  - Proceedings of the 2017 2nd Joint International Information Technology, Mechanical and Electronic Engineering Conference (JIMEC 2017)
PB  - Atlantis Press
SP  - 454
EP  - 457
SN  - 2352-538X
UR  - https://doi.org/10.2991/jimec-17.2017.100
DO  - 10.2991/jimec-17.2017.100
ID  - Tian2017/10
ER  -