We present an all-around study of the visitors flow in crowded museums: a combination of Lagrangian field measurements and statistical analyses enable us to create stochastic digital-twins of the guest dynamics, unlocking comfort- and safety-driven optimizations. Our case study is the Galleria Borghese museum in Rome (Italy), in which we performed a real-life data acquisition campaign. We specifically employ a Lagrangian IoT-based visitor tracking system based on Raspberry Pi receivers, displaced in fixed positions throughout the museum rooms, and on portable Bluetooth Low Energy beacons handed over to the visitors. Thanks to two algorithms: a sliding window-based statistical analysis and an MLP neural network, we filter the beacons RSSI and accurately reconstruct visitor trajectories at room-scale. Via a clustering analysis, hinged on an original Wasserstein-like trajectory-space metric, we analyze the visitors paths to get behavioral 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 fruition while respecting numerous logistic and safety constraints. This is possible thanks to optimized ticketing and new entrance/exit management.
Bibliographical noteFunding Information:
Results presented in this paper are achieved under the project Management of flow of visitors inside the Galleria Borghese in Rome, supported by Ministry of Cultural Heritage and Activities and Tourism, Galleria Borghese, and Istituto per le Applicazioni del Calcolo of National Research Council of Italy. Project's Principal Investigators are Marina Minozzi (Galleria Borghese) and Roberto Natalini (IAC-CNR).
E. Cristiani also acknowledges the Italian Minister of Instruction, University and Research to support this research with funds coming from the project entitled Innovative numerical methods for evolutionary partial differential equations and applications (PRIN Project 2017, No. 2017KKJP4X).
A. Corbetta also acknowledges the support of the Talent Scheme (Veni) research programme, through project number 16771, which is financed by the Netherlands Organization for Scientific Research (NWO).
This work was also carried out within the research project “SMARTOUR: Intelligent Platform for Tourism” (No. SCN_00166) funded by the Ministry of University and Research with the Regional Development Fund of European Union (PON Research and Competitiveness 2007–2013).
© 2021 The Author(s)
- Machine learning
- Museum optimization
- Museum simulator
- Tracking system