Combining Human-Centric Modeling and System Optimization in Rural Microtransit

This paper describes ongoing work integrating AI techniques for modeling humans and system optimization in the rural microtransit setting. We consider humans (riders) as central to the system, incorpo- rating their preferences while also accounting for system-wide benefit. Our goal is to develop a microtransit system that maximizes service efficiency, enabling more people to be served with the same resources without increasing costs for riders. We describe the architecture of our system and define our main use cases, as well as our piloting plans and other possible extensions of this work.