By Jessica Kent
November 06, 2020 – Researchers in Carnegie Mellon University’s Robotics Institute were able to use machine learning technology to reduce food insecurity in Pennsylvania communities during the pandemic.
The team leveraged advanced analytics tools to create cost-effective bus routes that allow nonprofit organizations to deliver meals to senior citizens, as well as K-12 students and families who would otherwise rely on schools for free meals. The machine learning tools identified ideal distribution locations to reach as many people as possible, three days a week.
The program began in July, and nearly 6,000 meals are delivered each month. It has since expanded to include dinners that feed a family of four.
Dig Deeper
Carnegie Mellon’s Metro21: Smart City Institute leads the project alongside Allies for Children, United Way of Southwestern Pennsylvania and the Greater Pittsburgh Community Food Bank. While the idea of route optimization originally came about a year ago, the team had to reimagine the plan after the onset of the pandemic.
“We originally talked to Allies for Children about a plan that would use machine learning to develop cost-effective bus routes transporting charter and private school students across school districts,” said Karen Lightman, Metro21’s executive director.
“When COVID closed schools in March and disrupted meal programs around the region, we pivoted. Instead of buses carrying students, we developed a program to have drivers bring lunches to families most in need.”
In Penn Hills, a township of Pennsylvania, many of the 4,000 students rely on free breakfast and lunch at school. Through a data sharing partnership between the Penn Hills School District and the Allegheny County Department of Human Services, CMU researchers gathered anonymized address information and entered it into a computer to identify locations and routes.
“The existing bus routes used to transport students weren’t ideal for meal distribution for a number of reasons,” said Stephen Smith, the research professor in the Robotics Institute who developed the delivery algorithms.
“Stopping every few blocks isn’t very efficient, and we needed areas where shuttles could safely stop, park and hand out food to groups of people. Our goal was to identify stops and routes to reach as many people as possible.”
Three times a week, prepared meals are transported to the Penn Hills Eat’n Park restaurant, where shuttle drivers load and disperse them throughout the city from 11am to 1pm on different routes. The shuttles stop on neighborhood streets, in parking lots behind fire stations, or at apartment complexes.
Each day, teams collect and report data on how many meals are passed out at each location. This allows the organization to adjust the plans as needed, while also giving researchers an opportunity to refine the machine learning algorithm.
Organizers are currently working with the Penn Hills School District to adapt the program now that school is back in session and schools are operating under a hybrid model.
“Penn Hills is a proud community, which makes it difficult at times to gauge accurately our level of need,” said Nancy Hines, superintendent of the Penn Hills School District.
“This project granted access to experts in the field with resources beyond our own, and the strategies that were implemented were done so in a very discreet and dignified manner, which helped us meet family needs which we, in isolation, could never have done. I sincerely thank CMU, Allies for Children, A Second Chance, United Way and our other partners on this very important project.”
Plans are currently underway to expand the system to different areas of the region. Carnegie Mellon recently received funding from the National Science Foundation to support this work and expand into other municipalities.
The team expects that this model could help reduce food insecurity for families across the state.
“This project, helping families in need, is among the most rewarding work Metro21 has ever done,” Lightman said. “It’s wonderful to see the impact on the community, not to mention the letters and feedback we’re getting from families and drivers. CMU’s work is making a difference in people’s lives, which is extremely satisfying, especially during these challenging times.”
Source: https://healthitanalytics.com/news/machine-learning-helps-reduce-food-insecurity-during-covid-19
