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  • Bluetooth sensors
  • Shorter stay visitors
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The visiting style of short (less than 1,5 hour) and long (more than 6 hours) stay visitors are not as significantly different as one could expect.
How much time would you take to smile back at the Mona Lisa? Today, sophisticated Bluetooth signal tracking allows us to map how visitors move through a museum like the Louvre in Paris – what galleries they visit, what path they take, and how long they spend in front of each piece of artwork. Join us for a look inside one of the world’s largest museums… to see the people in front of the paintings.

Visitor behavior and experience are among the most important factors informing museum management, with proven value in enhancing operations – yet data is traditionally generated by observations and surveys. These means ascertain visitors’ mediated perspectives on personal experience. The recent emergence of ubiquitous technologies has revolutionized the process of collecting data on human behavior, yet new digital means of data collection do not preclude

traditional approaches, each offering complementary value.

Consequently, the availability of large datasets based on quantified museum visitation patterns provides new opportunities to apply computational and comparative analytical techniques. In this pioneering study, we attempt to analyze visitors’ behavior in one of the world’s largest museums – The Louvre Museum – from anonymized longitudinal datasets generated by noninvasive Bluetooth sensors.

Our analysis discovered that the visiting style of short-stay (less than 1:30 min) and long-stay (more than 6 hours) visitors are not significantly different. Visitors in both categories tend to visit a similar number of key locations in the museum, yet long-stay visitors tend to do so more extensively. This disproves our initial hypothesis that short-stay visitors explore fewer of the popular places due to the time constraints.
The trajectories of long-stay visitors were hypothesized to be more complex than those of the short-stay visitors, and vice versa. However, our analysis implies that visitors’ trajectories seem to be quite limited in terms of the path sequence length and its order, although there exist a number of possible routes (including the repetition of certain nodes – that is, visitors return to a piece of art multiple times during one visit).

Finally, when visitors are offered a greater number of spaces, they tend to follow more selective paths. That is, when the number of the rooms with exhibits increases, visitors seem not to visit all of the exhibits, but target only a few of them selectively. Most importantly, this pattern is almost independent from the duration of a visitor’s stay – regardless of how long they spend in the museum, visitors to use the same trajectories.

» Read the paper

The research team deployed 7 Bluetooth sensors, with sufficient coverage to measure visiting sequences and duration at key representative locations. Sensors were arrayed along the main path from the entrance (Pyramid) to the Venus de Milo statue. Two were positioned on floor -1 (0 or Hall, 1 or Denon access); five were on floor 0 (2 or Denon 0, 3 or Samothrace 0, 4 or Venus de Milo, o 5 or Caryatides), and 3 on floor 1 (7 or Great Gallery, 8 or Samothrace 1, 9 or Glass).

The sensors recorded a unique encrypted identifier that distinguishes each Bluetooth-enabled mobile device within its range, as well as time stamps for entry and exit times. Assuming that a mobile device belongs to a person, we can relate the movement of the device to that of the visitor.

The study was conducted over a 24-day period with a high volume of visitor traffic. During this period, the array of sensors recorded the presence of 24,452 unique devices. A data cleaning process has removed security and museum staff traces by matching recurrence with the time of presence (e.g., outsider visiting time). In addition, two sensors were found to have an faulty time synchronization system and their logs were discarded.


On average, 8.2% of visitors activated Bluetooth on their mobile device in the Louvre Museum. Based on the paths derived from Bluetooth-enabled mobile devices and the number of ticket sales, we extrapolated visiting sequences and dwell times at key representative locations for the whole number of visitors. These data sets were integrated into microscopic crowd simulations which allow to examine visitors’ movement behavior in greater detail. This simulation-based prediction and analysis of visitor flows reveals valuable information such as crowd density, local congestions and capacity estimations.

Did you know?

The fastest recorded trip through the Louvre Museum is by Swiss artist Beat Lippert in 2010, clocked at 9 minutes and 14 seconds and recorded as an art piece La Sprezzatura. Yet he was hardly the first – his run was inspired by the three heroes of Jean-Luc Godard’s 1964 film Bande à part, who, with a bit of extra time on their hands, run through the museum in an attempt to break the previously standing record set by American Jimmy Johnson. Their pace certainly would skew our study – pulling down the average with a time of 9 minutes, 43 seconds.

Watch their careening visit here.

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The material on this web site can be used with the permission of the MIT Senseable City Lab and The Louvre Museum. Please contact to senseable-contacts@mit.edu or yyoshi@mit.edu.

Carlo Ratti
Lab director
Yuji Yoshimura
Project lead and study author
Stanislav Sobolevsky
Data analysis and study author
Yoon Chung Han
Visualization and website
Stefan Seer
Visualization and simulation
Pierrick Thébault
Visualization and website
Matthew Claudel
Fabien Girardin
Near Future Laboratory
Juan Pablo Carrascal
Universitat Pompeu Fabra
Josep Blat
Universitat Pompeu Fabra
Roberta Sinatra
Center for Complex Network Research and Department of Physicas, Northeastern University
Data Holders
Yuji Yoshimura & the Department of
Visitors Studies at the Louvre Museum