Reading Tea Leaves in the Tourism Industry: A Case Study in the Gulf Oil Spill

Hyunyoung Choi, Paul Liu
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引用次数: 13

Abstract

There has been significant interest from the travel industry in using search data to predict hotel bookings and other travel-related expenditures in advance. When we compared Google Trends data with a reference travel dataset from Smith Travel Research, Inc, we find that searches for travel take place on Google typically a few weeks to about a month before the actual travel. We then used time series techniques to forecast lodging demand in the Gulf region following the Gulf oil spill and estimated the impact of the oil spill. We found that demand in the non-Gulf region rose while demand in the Gulf region decreased. The findings were consistent at the state level and metro level.
解读旅游业中的茶叶:以墨西哥湾漏油事件为例
旅游业对使用搜索数据提前预测酒店预订和其他旅游相关支出非常感兴趣。当我们将Google Trends的数据与Smith travel Research, Inc .的参考旅行数据集进行比较时,我们发现,在Google上搜索旅行的时间通常是在实际旅行前几周到一个月。然后,我们使用时间序列技术来预测墨西哥湾漏油事件后海湾地区的住宿需求,并估计漏油事件的影响。我们发现,非海湾地区的需求上升,而海湾地区的需求下降。调查结果在州一级和城市一级是一致的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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