My family and I recently went on vacation to Disneyland, and I couldn’t help but notice the park’s use of data analysis to enhance guest experiences. The Disney app allowed us to check the wait times for each attraction, making it easier to plan our day. 

Although the predictions were mostly accurate, I noticed that the actual wait times deviated from the predicted wait times after a large, park-wide parade. As soon as the parade ended, the lines for nearby rides would increase from 5 minutes to 25 minutes within seconds. Because of this, I feel that Disneyland could possibly improve their model by including parades, rather than just factors like weather and time of day. 

Additionally, after seeing how wait time predictions could help us plan our day, I began wondering if there was an ‘ideal day at Disney’. Through further investigation, I came across a research study by Jake Mitchell that addressed this exact question. Mitchell applied a neural network to a MATLAB simulation that could determine how efficient a family’s day at Disneyland was. 

Mitchell created an AI model that could suggest the best ride to go on next, and he found several trends in his data. For example, he discovered that choosing a ride with a notoriously long line, such as Space Mountain, would ultimately lead to the group riding more attractions throughout the day. 

Mitchells ride suggestion tool is very helpful for families that enjoy visiting theme parks, and this type of technology could be implemented into more theme parks to improve guests’ experiences. I wish I had found this article before visiting Disneyland!