by Kara Manke (Reposted from the Duke Research Blog)

No parking spot? No problem.

A group of students has teamed up with Duke Parking and Transportation to explore how data analysis and visualization can help make parking on campus a breeze.

As part of the Information Initiative’s Data+ program, students Mitchell Parekh (’19) and Morton Mo (’19) along with IIT student Nikhil Tank (’17), spent 10 weeks over the summer poring over parking data collected at 42 of Duke’s permitted lots.

Under the mentorship of graduate student Nicolas-Aldebrando Benelli, they identified common parking patterns across the campus, with the goal of creating a “redirection” tool that could help Duke students and employees figure out the best place to park if their preferred lot is full.


A map of parking patterns at Duke

To understand parking patterns at Duke, the team created “activity” maps, where each circle represents one of Duke’s parking lots. The size of the circle indicates the size of the lot, and the color of the circle indicates how many people entered and exited the lot within a given hour.


“We envision a mobile app where, before you head out for work, you could check your lot on your phone,” Mo said, speaking with Parekh at the Sept. 23 Visualization Friday Forum. “And if the lot is full, it would give you a pass for an alternate lot.”

Starting with parking data gathered in Fall 2013, which logged permit holders “swiping” in and out from each lot, they set out to map some basic parking habits at Duke, including how full each lot is, when people usually arrive, and how long they stay.

However, the data weren’t always very agreeable, Mo said.

“One of the things we got was a historical occupancy count, which is exactly what we wanted – the number of cars in the facility at a given time – but we were seeing negative numbers,” said Mo. “So we figured that table might not be as trustworthy as we expected it to be.”

Other unexpected features, such as “passback,” which occurs when two cars enter or exit under the same pass, also created challenges with interpreting the data.

However, with some careful approximations, the team was able to estimate the occupancy of lot on campus at different times throughout an average weekday.

They then built an interactive, Matlab-based tool that would suggest up to three alternative parking locations based on the users’ location and travel time plus the utilization and physical capacity of each lot.

“Duke Parking is really happy with the interface that we built, and they want us to keep working on it,” Parekh said.

“The data team worked hard on real world challenges, and provided thoughtful insights to those challenges,” said Kyle Cavanaugh, Vice President of Administration at Duke. “The team was terrific to work with and we look forward to future collaboration.”

Hectic class schedules allowing, the team hopes to continue developing their application into a more user-friendly tool. You can watch a recording of Mo and Parekh’s Sept. 23 presentation here.


The team’s algorithm recommends up to three alternative lots if a commuter’s preferred lot is full. In this video, suggested alternatives to the blue lot are updated throughout the day to reflect changing traffic and parking patterns. Video courtesy of Nikhil Tank.


“Smart in parking lot” photo at web page top by Wikimedia user “GTI,” permission: CC by 3.0 unported.