There has been an increasing impact of tourism activities at popular destinations in recent years, leading to a phenomenon mostly called overtourism. This dissertation contributes to assess this problem by proposing a Smart Tourism Toolkit (STToolkit) for crowd-monitoring solutions. The STToolkit uses a sophisticated, flexible, low-cost and scalable crowding monitoring architecture composed by crowding sensors. Sensors may use multiple uplink options that mitigate the network limitations at the installation location of sensors. The number of people in sensor’s vicinity is detected in real-time, by counting the number of mobile devices in the same area, capturing trace elements generated by the normal usage of mobile devices in Wi-Fi. Challenges, like MAC address randomization, are addressed using fingerprinting techniques to uniquely identify devices. For validation, field experiments were conducted at Iscte’s campus to test the toolkit architecture. The fingerprinting technique was validated using a public available dataset and a dataset collected at Iscte. Finally, overcrowding sensors were also deployed at the National Palace of Pena to validate the final detection algorithm. The collected data is stored in a time-series database, and a data visualization platform is used to render crowding information. Overall, this dissertation concludes that the STToolkit can monitor the occupation of multiple locations in real-time, allowing space managers to perceive crowding tendencies and highly-populated events with effectiveness. With the provided supported material, the STToolkit emerges as a easily deployable and powerful tool to scaffold overcrowding management.

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Wi-Fi monitoring,MAC address randomization,Crowd sensor,Overtourism,Crowd detection

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