Data-driven Parking

Project Summary

ECE majors Mitchell Parekh and Yehan (Morton) Mo, along with IIT student Nikhil Tank, spent ten weeks understanding parking behavior at Duke. They worked closely with the Parking and Transportation Office, as well as with Vice President for Administration Kyle Cavanaugh.

Year
2016

Project Results

After extensive discussions with the data provider, the team was able to provide key insight into how to compute hourly occupation counts for all gated lots on campus, and also build a visualization tool (in Tableau) that elegantly displays these occupancy counts. In addition, they constructed a model that combines these occupancy counts with Google Maps data and computes the optimal "redirection lot'' for a driver to be directed to, in the event of over-capacity at the driver's normal lot. There are now discussions about integrating these visualization and redirection tools into the Parking and Transportation system.

Download the Executive Summary (PDF)

“The work of the Data+ team have exceeded my expectations. The intellectual curiosity and technical skill of the team have been outstanding. We are truly looking forward to operationalize the team’s work.” - Kyle Cavanaugh, Vice President for Administration of Duke
“I think that data+ was an immensely beneficial program for me. I was very happy to partake in it and can't wait to see it develop in the next few years. [I gained] an understanding for what big data really is, along with the amount of work that is required to extract usable information from it.”  - Mitchell Parekh, Electrical and Computer Engineering, Class of 2019

Faculty Lead: Robert Calderbank

Client: Parking and Transportation Office, Duke University

Project Manager: Nicholas Aldebrando Benelli, Economics

Participants:

  • Mitchell Parekh,  Duke University Electrical and Computer Engineering
  • Yehan (Morton) Mo, Duke University Electrical and Computer Engineering
  • Nikhil Tank, Indian Institute of Technology Electrical and Computer Engineering

Disciplines Involved

  • Economics
  • All quantitative STEM

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