AI Case Study
A UK registered charity predicts visitor flow to its building with Markov chain algorithms
A UK registered charity leveraged technology from ASI to predict visitor flow. The provider used wifi usage data to track crowd movement within the building and gather information on the amount of time people spent in each location. ASI created a model indicating the most likely routes people would take through the attraction, congestion points and locations prone to overcrowding using Markov chain algorithms. The algorithm simulated the movement of 500 different hypothetical visitors over a fifteen minute period to come up with the results.
Public And Social Sector
"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."
"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.
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."
"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."
"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."
Movement of people between access points within the building based on wifi usage data and amount of time people spent in each location.