paratransit usage, analyze its highest-cost rides, and more efficiently reschedule riders when a paratransit bus breaks down in the middle of a run.
One tool they built plots historical usage data—which can be overlaid with holidays, the closure of a community center, and other factors that might impact usage—to help Metro contract for only the paratransit service it needs for a given hour or day. For one particular Tuesday, the team found Metro could have contracted for 30 fewer hours of bus service, saving around $1,500, the team said.
Another tool could help dispatchers pick the least costly alternative to serve riders when the paratransit bus they were scheduled on breaks down. The tool automatically finds nearby busses that could be diverted to pick up stranded riders without causing riders on those busses to miss their appointments.
“Any sort of cost savings that we can provide immediately translates to the ridership of the scheduled [bus] system,” said Anat Caspi, director of the Taskar Center for Accessible Technology within the UW’s computer science department, in an interview earlier this summer. Caspi guided the paratransit team and a team building routing capabilities on top of Access Map, an app that identifies obstacles for people with limited mobility travelling through the city.
Career Opportunities in Urban Challenges
The interns were given tutorials on data management software such as SQL for database queries, ArcGIS for geospatial data, and Socrata, for open government data; programming languages Python and R; machine learning and analytics tools such as GraphLab; visualization tools from Tableau Software and D3, which was used for the homelessness diagram; and more.
DSSG, modeled on programs at the University of Chicago and Georgia Tech, is an outgrowth of the UW’s eScience Institute, a multipronged effort to advance data-driven discovery in all fields. Housed in a light-filled space on the sixth floor of the physics and astronomy building at the UW, the institute pairs subject-matter experts from departments across the university with data scientists who can help them apply the latest methods and technologies to data in their domain.
The idea is to bring the same data-intensive approach to research and problem solving that is now the coin of the realm in the hard sciences to urban challenges, training students along the way. It meshes with another new effort called Urban@UW, which is uniting researchers and practitioners from a range of fields, on and off campus, to tackle the multifaceted, integrated challenges facing cities in the 21st century, starting with Seattle. (Stay tuned for more coverage of Urban@UW, which will be featured in Xconomy’s upcoming Seattle 2035 conference this fall.)
Some 144 students applied to the initial 10-week DSSG internship program. Sixteen were selected, hailing from 10 different departments. The university students—graduates and undergraduates alike—were joined by six high school students from the UW’s Alliances for Learning and Vision for Underrepresented Americans (ALVA) program.
DSSG seemed to resonate with the participating students. It came through in their presentations, each of which ended with a laundry list of things they’d do next if they had more time.
Frank Fineis, a member of the paratransit team, is clearly aware of the opportunities unfurling in front of data scientists. Then he paraphrased Voltaire, or possibly Spiderman’s Uncle Ben, in describing potential data science career paths.
“It’s definitely a super-trendy buzzword,” he said. “It’s cool there’s this focus on social good, when I feel like there’s so many evil ways to use it, you know, like get hired working for a Raytheon. It’s cool that there’s this push for open data and for using your powers for good.”
