Optimizing service schedules is pivotal to reliable, efficient, and inclusive on-demand mobility. This challenge is especially acute for complex paratransit systems that must jointly coordinate rider trip planning and crew scheduling under tight real-time constraints.
This work develops a graph-neural-network-informed column generation approach for the Joint Rider Trip Planning and Crew Shift Scheduling Problem. The key idea is to reduce the number of paths explored in the pricing problem, accelerating the most time-consuming part of column generation while preserving solution quality. The method was evaluated on a real-world dataset from the paratransit system of Chatham County in Georgia and produced substantial improvements over baseline approaches. Additional project context is available on the SAM lab site.