Theme Park Routing: A Decision Support System for Walt Disney World Trips

With over 52 million visitors annually, the Walt Disney World theme park is one of the busiest places on earth (US City Traveler, 2015). While some of these visitors are regular attendees, most are creating new memories in unfamiliar territory. To assist these novice theme park visitors, a plethora of reference books, blogs, tour guides and other resources exist. These recommend which attractions to visit and their popularity. What these resources do not provide, however, is the optimal order that these attractions should be visited

Determining Theme Park Attraction Attributes: An Analysis of Factors that Impact Theme Park Attraction Popularity and Success

Theme parks have some attractions that are more popular than others, referred to as main ticket attractions (MTA). The purpose of this thesis is to create a model which can successfully predict whether or not theme park attractions are considered MTA. Data from leading USA theme park attractions has been recorded and analyzed for this thesis. A neural network model has been created using Matlab that categorizes attractions with up to 85% accuracy. However, some of the inputs are considered unstable once run through SAS JMP. To create a comparative study, a decision tree has been created in Matlab with the same 15 inputs. Five attractions were withheld from the models to compare their results. In the end, the decision tree categorized 90% of the attractions correctly, while the neural network categorized 80% appropriately