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Centro di Eccellenza DTC Lazio
Esperienze di controllo dei flussi dei visitatori in ambientimuseali: il caso della Galleria Borghese a Roma
C. Balzotti, M. Briani, A. Corbetta, E. Cristiani, M. Minozzi, R. Natalini, S. Suriano, F. Toschi
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Mathematical modelling vs computer graphics
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The Galleria Borghese and the projectThe Galleria Borghese is one of the most important museums in the world. Despite the small size of the exhibition space, the museum has more than500.000 visitors/year. Tickets must be reserved in advance, museum is always sold out.5 turns of visit/day (2 hours each), 360 people/turn.2 floors, 3 entrances, circular shape, no predefined exhibition path.
The project involves IAC‐CNR, GB, ISCR e MiBACT and began on July 2017. The goals are to measure, analyze, forecast, control and optimize the pedestrian flows inside the museum in order to improve its fruition. Also, the project aims at quantifying the impact of visitors on microclimateand degradation of works of art exhibited.
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PHASE 1
Manual data acquisition
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DATA ACQUISITIONGoals:• measuring how many people are in every room at every time• measuring the time spent by every visitor in every room
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DATA ACQUISITIONWe formed a team composed by 53 people who collected more than 30,000 data in 4 turns of visit (on 4 different days).
Photos ©Andrea Sabbadini
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Photos ©Andrea Sabbadini
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DATA ANALYSIStime spent in every room
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PHASE 2
Creation of a predictivemathematical model
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THE MATHEMATICAL MODELmain ideas
The complex behaviour of visitors inside the museum makes the prediction of the path of each single visitor very complicated (machine learning algorithms?). On the contrary, it is quite easy to predict the movements of the visitors as a whole.
For this reason we adopt an “Eulerian” point of view, being interested only in the number of people present in every room at all times.
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THE MATHEMATICAL MODELmuseum as a graph
The visitors’ dynamics deploy over an undirected graph representing the museum rooms. Movements through rooms are ruled by stochastic variables.
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A NEW TICKETING STRATEGY
TIME OVER 130: 86 min (‐24%)
VISITORS:+40 per day
EXCESS OVER 90: 12 kv∙min (+39%)
Entrance every hour
Free visit
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PHASE 3
Automatic data acquisition
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Several data acquisition systems exist:
• Cameras• Smartphones• Laser scanners• RFID tags• Beacons BLE
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BEACON BLE 4.2
Maximal distance: 50 m
Reader Raspberry Pi 3 B+
tag
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PROTOTYPE
• 1 modem 4G• 3 Raspberry Pi 3 B+• 3 Wi‐Fi repeaters• 9 beacons
Rooms monitored: 1, 2, 3
Python code on RPi’sscanning beacons and sending packets to CNR server every 5 sec
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PROTOTYPE’S RESULTS (1 beacon)raw data
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PROTOTYPE’S RESULTS (1 beacon)processed data
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PROTOTYPE’S RESULTS (1 beacon)Inferred visitor’s path
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WORK IN PROGRESS
We are currently extending the visitors’ tracking system to the whole museum (both floors) in order to track at least the half of visitors per turn. Immediate goals are• creating a huge database of paths• improve the mathematical model• quantifying the interest for every room and possibly for
every single artwork• quantifying the usage and the impact of the audioguides• detecting illegal groups and anomalies in real time
C. Balzotti, M. Briani, A. Corbetta, E. Cristiani, M. Minozzi, R. Natalini, S. Suriano, F. Toschi, Forecasting visitors’ behaviour in crowded museums. A case study: The 'Galleria Borghese' in Rome, Proceedings PED2018 (Lund, August 21‐24, 2018), to appear.