Clustering Theme Park Guest Movements, Habits, and Prioritizations for Pedestrian Modeling

Abstract

The intention of this research is to divide theme park guests into clusters based upon their movement preferences and behaviors into waders, swimmers, and divers and provide the groundwork for improved theme park pedestrian modeling through data driven parametrization of digital agents. To create this informed groundwork, data collection at three theme parks across Southern California is conducted, consisting of GPS tracking data, a survey, and a log of a participant’s theme park day using the GPS tracks. These collection outputs are combined to form parameters with associations to definitions of waders, swimmers, and divers to cluster guests into groupings. Comparing these clusters to measured data and survey responses validates algorithmic cluster groupings, demonstrates that clustering varies across parks with different audiences, and provides parameters that can be used to inform the weighted preferences of digital agents in simulations, improving the theme park experience.