Math Predicts Theme Park Attractions Lines
As theme parks crowds start to build over the summer, UCF researchers are trying to figure out how visitors can avoid the long lines.
The company Touring Plans sponsored a competition asking researchers to leverage big data to help predict long lines at multiple Walt Disney World attraction.
The competitors were asked to predict wait times up to a year in advance on rides like Splash Mountain and Soarin’. They used thousands of files that contained wait times over several years, opening and closing times and temperature records.
Each competitor developed a statistical model to help decipher the data and predict the wait times for future visits.
Phyong Pho, a graduate teaching assistant in the economics department and co-winner, said the project was one of the most time-consuming he’s worked on. He said he tried numerous statistical models until he found one that worked.
“I really enjoyed the Big Data Challenge because it gave me a great opportunity to apply the modelling methodology I learned in school to real-life problems,” he said. “It’s satisfying when you find the meaningful function form or the interaction that improved the predictability of the model.”
Two UCF alumni, Kanak Choudhury and Taha Mokfi, share the first place prize with Pho.
Touring Plans wants to use the models to help future tourists maximize their time at the parks.
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