Using Data Analytics to Optimize Public Transportation on a College Campus

Using a large volume of bus data in the form of GPS coordinates (over 100 million data points) and automated passenger count data (over 1 million data points) we have developed (1) a system of analysis and prediction of future public transportation demand (2) a new model that uses concepts specific to college campuses that maximizes passenger satisfaction. Using these concepts, we improve service of a model college public transportation service and more specifically the Indiana University Campus Bus Service (IUCBS) by decreasing overall fuel usage by nearly 20% while increasing passenger participation by 15%. The scheduler, which forms the nexus of the system, is generated with a modified simulated annealing algorithm using the entire class schedule for the entire campus of all students, then measured against previous traffic data to select a near optimal new schedule.