Smart Tourism Toolkit for
crowd monitoring solutions
crowd monitoring solutions
The commonality between SMEs in cultural tourism and those that organize congresses and other events is that they have to monitor the occupation of the space where their activity takes place, either to ensure a better space management of attendees (e.g. to forward crowds to areas with lower occupancy), for security reasons (e.g. to avoid tampering with exposed objects in a museum space), health reasons (e.g. to avoid exceeding the maximum density of people allowed by health authorities in an event space to prevent contagion in times of pandemic), or simply because of workforce management considerations (e.g. to calculate the expected time for evacuating a concert arena to allow cleaning teams to proceed). An STT to monitor crowding levels in real-time and/or estimate the outflow period is, therefore, an innovative tool for optimizing the delivery and quality of the service provided by the aforementioned SME tourism operators.
Inside view of our crowd sensors
This toolkit is based on crowd sensors that can detect the number of mobile devices in their vicinity based on their wireless activity (Wi-Fi and/or Bluetooth). The sensors can be deployed at points of interest and measure the number of people at each location and perceive how crowded they are in real time and perform retrospective analysis.
The number of people in the 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.
In February/March 2024, the RESETTING@Iscte team in charge of the STT for crowd monitoring solutions participated in the CONFRONT – Challenge ON wifi FRame fingerprinting for people cOunting aNd Tracking, an activity funded by MOST (Centro Nazionale per la Mobilità Sostenibile), Ministero dell'Università e della Ricerca, Italy.
This challenge consisted of three tasks, of increasing difficulty. Seven participants from four countries (Germany, Italy, Portugal, and Slovenia) applied their fingerprinting algorithm to a provided dataset. In our case, this is the algorithm running on our edge nodes.
With a score of 295.92 out of 300.00 the submission of the RESETTING@Iscte team was the winner!
For more details click on the podium icon.
Rui Neto Marinheiro
(Task Manager)
Fernando Brito e Abreu
(Senior Researcher)
Tomás Mestre Santos
(PhD Fellow)
Tiago Filipe Vieira
(MSc Fellow)