Seniors in the Department of Civil and Systems Engineering (CaSE) are spending their final year turning classroom knowledge into community-focused engineering designs. The CaSE capstone projects are culminating academic assignments that require teams to address real problems through actionable engineering solutions. The students are divided into two teams according to their major—civil engineering and systems engineering—with both groups working with their designated clients to improve life for Baltimore City residents.
Both teams showcased their work at Design Day 2026!
Systems engineering majors Diana Arizmendi, Benjamin Casino, Charmi Daas, and Diran Jimenez, are tackling unreliable public transit for City Schools students. Working with Johns Hopkins University Professor Julia Burdick-Will, whose research shows that frequent bus transfers and unreliable service contribute to low first-period attendance, the group is building an optimization tool that will improve transit routes and ride times for high school students.
“Students who depend on public transit to get to and from school don’t have options that are consistently timely or reliable,” said Jimenez. “There are direct links between reliable transportation and improved academic performance, but neither the Maryland Transit Administration nor City Schools have direct responsibility for a student transit system.”
Current bus routes, census tracts, and high schools in Baltimore City (left) and an overlay of current and optimized bus routes (right)
The team is using ArcGIS mapping of census tracts and existing bus routes to better understand existing commuting patterns and student rider experiences. They’re now developing and writing the code for a genetic algorithm, an optimization technique that iteratively evolves to find optimal solutions based on specified criteria. The group says that their algorithm combines strong route segments and introduces small random changes based on parameters like stop locations, number of buses in operation, start and end points, travel times between stops, roadway constraints, school schedules, and vehicle capacities. As part of the code, they’ve added penalties for scenarios they want to avoid, such as ride times longer than 60 minutes and multiple transfers.
“We want the solution to be simple and inexpensive to implement, but also flexible so students can adapt if they miss a bus,” Casino said. “We’re looking at scenario metrics, cost analysis, and equity in terms of making sure that times are averaged among riders to prevent one student from having a 15-minute commute while another has a 60-minute commute.”
Arizmendi, Casino, Jimenez, and Daas are evaluating various options to improve the student rider experience, however they anticipate that their final solution will be a combination of modifying MTA routes throughout the city and supplementing routes with dedicated yellow buses, which they say could lead to improvements for other riders.
“Improving student transit would likely have the effect of improving transit for all riders,” said Daas. “Transit affects access to opportunities, and low-income neighborhoods are disproportionately harmed by unreliable service. Our project is focused on high school commutes, but better routes and reliability benefit the whole community.”
The group says that their genetic algorithm will be transferable, so it can be reused by MTA year-to-year to incorporate changes in student ridership, routes, and driver schedules. It can also be updated to accommodate other parameter changes, like school start times, roadway closures, or even broadening the scope to accommodate middle school commuters.
“Systems engineering is all about the tools you have at your disposal, narrowing parameters, and asking thoughtful questions, so that you can take a large-scale, nebulous problem and design evidence-based solutions,” said Casino. “These can be scaled and applied to almost any challenge.”