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Department of Applied Physics, Turbulence and Vortex Dynamics
*Contacts:
a.corbetta@tue.nl
http://corbetta.phys.tue.nl/
Introduction
Operating a museum, with its intricate assembly of rooms, entails
a complex issue: the optimization of the pedestrian floor usage
(e.g. preventing overcrowding, ensuring queue-less fruition of
works of art/rooms, etc.). This is essential to ensure visitors’
safety and comfort.
A successful floor usage optimization demands monitoring,
forecasting,and, ultimately, steering the visitors’
dynamics. High-quality, real-time, visitors’ monitoring is the first
building block.
Galleria Borghese, boasting a world-wide unique art collection,
features more than 500.000 visitors per year. It comes with a
circular space distributed over two floors and no suggested
visiting path.Complex pedestrian dynamics develop,
encompassing low and high densities. As such, it is a perfect
location to develop monitoring techniques and to test forecast and
steering strategies.
We realized a visitor indoor positioning system,with room-
scale resolution, which enables us to determine, closely to real-
time, the room in which each visitor is.
This information enables, Lagrangian tracking and Eulerian
statistics (e.g. room occupancy, average visiting time per room,
etc.), both at room scale.
Radio-based visitors tracking
We track anonymously each visitor of Galleria Borghese by
means of radio beacons (Bluetooth low energy-based) that are
individually provided at the entrance, and returned at the end of
the visit.
Each beacon periodically broadcasts its identity.In every room,
we positioned one or more antennas, that we built with Raspberry
Pi computing units.The antennas record these signals
communicate to a central server the beacon identity and signal
strength (rssi). See Figures 3-5 for first quantitative tracking
results.
Figure 1. (A) An example of antenna (on the left) and beacon (on the
right).(B) Beacon as it is generally wore by the museum visitors.
Optimizing museums’ experience
via trajectory analysis of visitors
A case study: the Galleria Borghese in Rome
A.Corbetta1,*, E.Cristiani2, M.Minozzi3, E.Onofri2, F.Toschi1,2
1Eindhoven University of Technology, NL; 2CNR-IAC, Rome, IT; 3Galleria Borghese, Rome, IT.
Figure 3. Room-scale trajectory of the visitor in Figure 2 across the first
floor of the museum. We report the measured entrance time for each
room and compare it with ground truth.The positions of the Raspberry
Pi antennas are highlighted with blue squares.
Figure 2. Signal strength (RSSi)of a single beacon (visitor) as received
from different antennas.We associate the visitor to the room by
considering the antenna (room) with stronger signal reading.
Figure 4. Floor-scale
crowd detection. We
report how many visitors
are detected time by time
in the two floors of the
museum.
(results extrapolated from
42 beacons to actual
visitors number ~300)
Figure 5. Room-scale
average estimated time
of visit (ETV).
Future Developments
We are going to use Machine Learning and IoT in order to
improve visitors tracking and prevent over-congested periods.We
also plan to develop Museums’ Digital Twins in order to build an
offline simulation and optimization tool for visitors flow
management.