In this talk, we present an all-around study of the visitors’ flow in crowded museums: a combination of Lagrangian field measurements and statistical analyses enables us to create stochastic
digital-twins of the guest dynamics, unlocking comfort- and safety-driven optimisations.
Specifically, we employ a Lagrangian IoT-based visitor tracking system relying on Raspberry Pi
receivers, displaced in fixed positions throughout the museum rooms, and on portable Bluetooth
Low-Energy (BLE) beacons handed over to the visitors. The signal intensity provides a proxy for
the distance to the antennas and thus indicative positioning. However, RSSi signals are well-known
to be noisy, even in ideal conditions (high antenna density, absence of obstacles, absence of crowd,
etc.). We present a cascaded AI-classifier method to perform accurate RSSi-based visitor tracking
when the density of antennas is relatively low allowing us to reconstruct visitor trajectories at
room-scale thanks to a convenient encoding of the museum topology in terms of a total-coloured
graph.
Via a clustering analysis, hinged on an original Wasserstein-like trajectory-space metric, we analyse the visitors’ paths to get behavioural insights, including the most common flow patterns. On
these bases, we build the transition matrix describing, in probability, the room-scale visitor flows.
Such a matrix is the cornerstone of a stochastic model capable of generating visitor trajectories in
silico.
We conclude by employing the simulator to enhance the museum’s fruition while respecting numerous logistic and safety constraints. This is possible thanks to optimised ticketing and new
entrance/exit management. Our case study are the Galleria Borghese Museum in Rome and the
Peggy Guggenheim Foundation in Venice (Italy), in which we performed multiple real-life data
acquisition campaigns.
References
[1] Pietro Centorrino, Alessandro Corbetta, Emiliano Cristiani, and Elia Onofri. Measurement and analysis
of visitors’ trajectories in crowded museums. In IMEKO TC-4, pages 423–428, Florence, Italy, 12 2019.
International Conference on Metrology for Archaeology and Cultural Heritage. imeko:2019-83.
[2] Pietro Centorrino, Alessandro Corbetta, Emiliano Cristiani, and Elia Onofri. Managing crowded museums:
visitors flow measurement, analysis, modeling, and optimization. Journal of Computational Science, 53:1–
17, 04 2021. doi:10.1016/j.jocs.2021.101357.
[3] Elia Onofri and Alessandro Corbetta. RSSi-based visitor tracking in museums via cascaded AI classifiers
and coloured graph representations. Collective Dynamics, 6:1—17, 01 2022. doi:10.17815/CD.2021.131.
[4] Pietro Centorrino, Emiliano Cristiani, Pietro Ferrara, Danilo Macchion, and Elia Onofri. Measurement
and analysis of the visitors behavior in the Peggy Guggenheim collection. Technical report, IAC–CNR, 2
2023.