Identification of Mobility Patterns of Clusters of City Visitors: An Application of Artificial Intelligence Techniques to Social Media Data

Abstract

In order to enhance tourists’ experiences, Destination Management Organizations need to know who their tourists are, their travel preferences, and their flows around the destination. The study develops a methodology that, through the application of Artificial Intelligence techniques to social media data, creates clusters of tourists according to their mobility and visiting preferences at the destination. The applied method improves the knowledge about the different mobility patterns of tourists (the most visited points and the main flows between them within a destination) depending on who they are and what their preferences are. Clustering tourists by their travel mobility permits uncovering much more information about them and their preferences than previous studies. This knowledge will allow DMOs and tourism service providers to offer personalized services and information, to attract specific types of tourists to certain points of interest, to create new routes, or to enhance public transport services.

A Lightweight Approach for Building User Mobility Profiles

“…By analyzing the data generated by a mobile device, it is possible to learn different aspects of users' mobility, such as the places visited, the routes taken to reach them and the activity carried out in those places. These data allow the construction of user mobility profiles [3,4], which are useful both at the individual level, for the development of context-sensitive services [5,6] and at the group level, in the context of smart cities and tourism [7][8][9].…”