We evaluated the performance of the bus system serving the College of Engineering and developed data-driven recommendations to improve reliability, efficiency, and user experience. Increasing enrollment and new academic facilities prompted our study, as these changes were projected to generate higher passenger demand on the Innovation route.
We applied a DMAIC methodology to define the problem, measure performance, analyze root causes, and design improvements. We conducted field Gemba observations on each route to collect operational data, including arrival times, dwell durations, headways, passenger counts, and schedule adherence. We also analyzed StarMetro data and rider feedback to estimate demand patterns and identify peak congestion periods.
Using these data, we developed a discrete-event simulation model in Simio to represent operations, accounting for class rush surges, vehicle capacities, and operator breaks. We tested several scenarios, including bus additions, headway adjustments, break relocations, and modified stop sequences. We compared performance metrics—specifically average wait time, cycle time, and hourly capacity, to determine feasible improvements within existing resource constraints.
Our analysis revealed gaps in schedule consistency and underutilization during off-peak periods, whereas peak periods exhibited crowding and uneven vehicle spacing. We produced an optimization framework that matched student travel demand to available trips and validated solutions through simulation. These results inform proposed route and scheduling changes aimed at reducing commute times while maintaining cost efficiency. Our findings provide StarMetro and the College of Engineering with actionable recommendations to support future growth and dependable transportation services.
