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• W2891382305 abstract "Reliable travel behavior data is a prerequisite for transportation planning process. In large tourism dependent cities, tourists are the most dynamic population group whose size and travel choices remain unknown to planners. Traditional travel surveys generally observe resident travel behavior and rarely target tourists. Ubiquitous uses of social media platforms in smartphones have created a tremendous opportunity to gather digital traces of tourists at a large scale. In this paper, we present a framework on how to use location-based data from social media to gather and analyze travel behavior of tourists. We have collected data of about 67,000 users from Twitter using its search interface for Florida. We first propose several filtering steps to create a reliable sample from the collected Twitter data. An ensemble classification technique is proposed to classify tourists and residents from user coordinates. The accuracy of the proposed classifier has been compared against the state-of-the-art classification methods. Finally, different clustering methods have been used to find the spatial patterns of destination choices of tourists. Promising results have been found from the output clusters as they reveal most popular tourist spots as well as some of the emerging tourist attractions in Florida. Performance of the proposed clustering techniques has been assessed using internal clustering validation indices. We have analyzed temporal patterns of tourist and resident activities to validate the classification of the users in two separate groups of tourists and residents. Proposed filtering, identification, and clustering techniques will be significantly useful for building individual-level tourist travel demand models from social media data." @default.
• W2891382305 created "2018-09-27" @default.
• W2891382305 creator A5011052200 @default.
• W2891382305 creator A5056217950 @default.
• W2891382305 date "2018-11-01" @default.
• W2891382305 modified "2023-10-09" @default.
• W2891382305 title "Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data" @default.
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• W2891382305 doi "https://doi.org/10.1016/j.trc.2018.09.006" @default.
• W2891382305 hasPublicationYear "2018" @default.
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