As transit agencies test out emerging technologies for providing service, they encounter a challenge of having limited data compared to conventional transit operations with well-designed survey processes and an existing rider base. In such circumstances, the selection of new routes need to play a double role, not just to provide access to travelers, but also to act as “sensors” to learn how the demand responds. We developed a framework for incrementally growing out such a transit network that uses these optimal learning techniques to decide where next to expand. His tests identify the circumstances when “knowledge gradient” policies with correlated beliefs can best be fashioned into such sequential transit network design processes — a next step toward AI-driven transit network evolution that can help expedite the adoption of new transit technologies and policies.

Paper can be found here: https://www.sciencedirect.com/science/article/pii/S1366554524002989