Predicting Visitor Flow

Housing a vast collection of exhibits, the internationally renowned attraction is the third most visited in the UK, receiving over 5 million visitors in 2015.

Situation

With so many visitors, good crowd management is vital, having implications not only for security, but for the ability of cafes and shops to provide better services and increase revenue. As a registered charity reliant on donations, customer satisfaction takes on even greater importance.

The staff have previously managed crowds well, albeit without the use of data.


Action

ASI took a data science approach to model and predict visitor flow. It was possible to use wifi usage data to track crowd movement as people travelled between access points within the building, as well as to collate information on the amount of time they spent in each location. By combining tens of thousands of these journey maps, it was possible to see certain patterns among visitors emerge.

Using Markov chain algorithms, ASI implemented a model simulating the movement of 500 different hypothetical visitors over a fifteen minute period, if they all started out in the same place and time.

The model created a clear picture of the different routes people would be most likely to take as they moved through the attraction. It identified not just congestion points, but the times when key locations were prone to overcrowding.

Predicting Visitor Flow
A floor-plan of the attraction, showing the route taken by one individual visitor, constructed from wifi access point data.

Impact

The project’s findings have important implications for the attraction. They offer a model from which staff can develop more accurate, quantitative crowd management, leading to increased customer satisfaction and increased revenue.

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