Managing crowded museums: Visitors flow measurement, analysis, modeling, and optimization

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 safetydriven optimizations

P. Centorrino; A. Corbetta; E. Cristiani; E. Onofri


Scholarcy highlights

  • The research unfolds along the following lines: 1. We describe a cheap and reproducible data collection system, hinged on an IoT-based room-scale Lagrangian tracking system of the museum visitors
  • We present our methods and original contributions alongside our field activity at Galleria Borghese museum, our case study
  • We develop a digital twin of the museum, i.e. an algorithm which is capable of generating new trajectories, indistinguishable from measured ones
  • In order to simulate the complex visitor behavior, we introduce a memory in the Markov Chain, to represent the visitors knowledge of the visited rooms
  • We employ the simulator to significantly increase the efficiency of the ticketing strategy and entrance/exit management
  • Control variables: C1: considering that Galleria Borghese has three entrances, museum managers can assign a certain percentage of visitors to each entrance
  • The entry scheme identified is to let 100 people enter every 30 minutes from Portico and Ratto di Proserpina while eliminating the 2h time limit, reducing congestion and fluctuations of the number of people in each room

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