Smart(er) Routing at Theme Parks

Project Summary

Runliang Li (Math), Qiyuan Pan (Computer Science), and Lei Qian (Masters in Statistics and Economic Modelling) spent ten weeks investigating discrepancies between posted wait times and actual wait times for rides at Disney World. They worked with data provided by TouringPlans.

Themes and Categories

Project Results

The team built a linear regression model to predict future wait times on given rides based on historical wait times on many other rides, time of day, season, and many other factors. Their model was informed by a lot of exploratory data analysis, as well as much data cleaning and merging.

Download the Executive Summary (PDF)

Faculty Sponsors

Participants

  • Qiyuan Pan, Duke University Computer Science
  • Runliang Li, Duke University Computer Science & Mathematics
  • Lei Qian, Duke University Statistical and Economic Modeling

Project Manager

Disciplines Involved

  • Business Analytics
  • Operations Research
  • All quantitative STEM

Related People

Related Projects

United Nations Sustainable Development Goal 7 calls for universal access to affordable, reliable, sustainable, and modern energy. Researchers and practitioners around the world have responded to this call by producing a wealth of energy access data. While many data gaps still exist, are we capturing the fullest potential from the information and research we do have, and what it tells us about how to accelerate energy access? Power for All’s Platform for Energy Access Knowledge (PEAK) is an interactive knowledge platform designed to automatically curate, organize, and streamline large, growing bodies of data into digestible, sharable, and useable knowledge through automated data capture, indexing, and visualization. A team of students led by Rebekah Shirley will consult with Power for All to creatively visualize PEAK’s library, and to explore machine learning and natural language processing tools that can enable auto-extraction and visualization of data for more effective science communication.

Are there relative value opportunities in the global corporate bond markets?  
A team of students will work with Professor Emma Rasiel to understand whether an analysis of credit spreads on bonds issued by international firms in multiple countries over time can shed light on potential arbitrage opportunities. The team will have frequent opportunities to interact with analytics professionals at a leading financial advisory and asset management firm.

 

A team of students will consult with a leading financial advisory and asset management firm that is seeking to understand how big data can shed light on the secondary market for construction machinery. Students will explore a combination of publicly-available datasets that describe the used-machinery market and its potential implications as an indicator for the business cycle. There will be frequent interactions with analytical professionals from the firm.