An Analysis of Visitors' Behavior in the Louvre Museum: A Study Using Bluetooth Data
By Yuji Yoshimura, Stanislav Sobolevsky, Carlo Ratti, Fabien Girardin, Juan Pablo Carrascal, Josep Blat, Roberta Sinatra. 2014
173 citations recorded by the source corpus.
Museums often suffer from so-called ‘hypercongestion’, wherein the number of visitors exceeds the capacity of the physical space of the museum. This can potentially be detrimental to the quality of visitors' experiences, through disturbance by the behavior and presence of other visitors. Although this situation can be mitigated by managing visitors' flow between spaces, a detailed analysis of visitor movement is required to realize fully and apply a proper solution to the problem. In this paper we analyze visitors' sequential movements, the spatial layout, and the relationship between them in a large- scale art museum—The Louvre Museum—using anonymized data collected through noninvasive Bluetooth sensors. This enables us to unveil some features of visitor behavior and spatial impact that shed some light on the mechanisms of museum overcrowding. The analysis reveals that the visiting styles of short-stay and long-stay visitors are not as significantly different as one might expect. Both types of visitors tend to visit a similar number of key locations in the museum while the longer-stay visitors just tend to do so more time extensively. In addition, we reveal that some ways of exploring the museum appear frequently for both types of visitors, although long-stay visitors might be expected to diversify much more, given the greater time spent in the museum. We suggest that these similarities and dissimilarities make for an uneven distribution of the number of visitors in the museum space. The findings increase the understanding of the unknown behaviors of visitors, which is key to improving the museum's environment and visitor experience.
Subfields: Electrical and Electronic Engineering, Ocean Engineering, Transportation
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In Electrical and Electronic Engineering
- Billy Pik Lik Lau
- U-Xuan Tan
- Yuren Zhou
- Meng Zhang
- Naveed Ul L. Hassan
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- Chau Yuen
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In Transportation
Citations in the corpus
- A survey of data fusion in smart city applications (2019, background): “…and understanding human mobility include distance and duration distributions [191], origin-destination matrices [103], individual activity-based mobility patterns [192], transportation mode identification [54], and densities and flows within a building (or a cluster of buildings) [138], [193].”
- Fostering customers' pro-environmental behavior at a museum (2017, background): “Indeed, visitors’ behavior, along with their experiences, is regarded as one of the most critical factors for museum management (Yoshimura et al., 2014).”