Abstract
Crowd monitoring was an essential measure to deal with over-tourism problems in urban destinations in the pre-COVID era. It will play a crucial role in the pandemic scenario when restarting tourism and making destinations safer. Notably, a Destination Management Organisation (DMO) of a smart destination needs to deploy a technological layer for crowd monitoring that allows data gathering in order to count visitors and distinguish them from residents. The correct identification of visitors versus residents by a DMO, while privacy rights (e.g., Regulation EU 2016/679, also known as GDPR) are ensured, is an ongoing problem that has not been fully solved. In this paper, we describe a novel approach to gathering crowd data by processing (i) massive scanning of WiFi access points of the smart destination to find SSIDs (Service Set Identifier), as well as (ii) the exposed Preferred Network List (PNL) containing the SSIDs of WiFi access points to which WiFi-enabled mobile devices are likely to connect. These data enable us to provide the number of visitors and residents of a crowd at a given point of interest of a tourism destination. A pilot study has been conducted in the city of Alcoi (Spain), comparing data from our approach with data provided by manually filled surveys from the Alcoi Tourist Info office, with an average accuracy of 83%, thus showing the feasibility of our policy to enrich the information system of a smart destination.
Highlights
The smart destinations concept was coined as a distinct step in the evolutionary relationship of ICT (Information and Communications Technologies) and tourism, characterised by integrating the physical and the digital world [1]
A Destination Management Organisation (DMO) of a smart destination needs to deploy a technological layer for crowd monitoring that allows data gathering in order to count visitors and distinguish them from residents
We describe a novel approach to gathering crowd data by processing (i) massive scanning of WiFi access points of the smart destination to find SSIDs (Service Set Identifier), as well as (ii) the exposed Preferred Network List (PNL) containing the SSIDs of WiFi access points to which WiFi-enabled mobile devices are likely to connect
Summary
The smart destinations concept was coined as a distinct step in the evolutionary relationship of ICT (Information and Communications Technologies) and tourism, characterised by integrating the physical and the digital world [1]. As stated by [13], this is still considered an open problem for smart tourism destinations, and it is an initial step to further consider tourist digital footprints or data traces from tourist activities (as they occur if a person can be considered a tourist) [13] To this end, the main contribution of this paper is an approach to (i) gathering crowd data by processing smartphone device signals based on WiFi scanning, as well as (ii) differentiating the percentage of visitors and residents in a smart tourism destination. Initial results show that our approach is a viable solution that could help the DMO to make informed decisions and offer visitors a secure experience, while keeping residents safe, avoiding overcrowding in the pandemic scenario At this point, it should be noted that our approach complements other types of initiatives from DMOs based on the purchase of data from third parties (mobile providers).
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