Fine-grained tourism prediction: Impact of social and environmental features
作者：
Highlights：
• Exploiting Social Media and Environmental features to perform accurate fine-grained (attraction-level) predictions.
• Accurate prediction results (MAPE < 25%) for more than 93% of the indoor and outdoor attractions.
• Collection, join and analysis of 5 different datasets containing two types of attractions - indoor and outdoor.
• Better performance of Environmental features in outdoor attractions such as parks versus indoors like museums and galleries.
• Complete Failure analysis for attractions in which proposed prediction methodology could not produce satisfactory results.
摘要
•Exploiting Social Media and Environmental features to perform accurate fine-grained (attraction-level) predictions.•Accurate prediction results (MAPE < 25%) for more than 93% of the indoor and outdoor attractions.•Collection, join and analysis of 5 different datasets containing two types of attractions - indoor and outdoor.•Better performance of Environmental features in outdoor attractions such as parks versus indoors like museums and galleries.•Complete Failure analysis for attractions in which proposed prediction methodology could not produce satisfactory results.
论文关键词：Tourism demand prediction,Fine-grained prediction,Time-series analysis,Social media data,Environmental data
论文评审过程：Received 15 November 2018, Revised 10 June 2019, Accepted 12 June 2019, Available online 21 June 2019, Version of Record 13 January 2020.
论文官网地址：https://doi.org/10.1016/j.ipm.2019.102057
