In this collaborative work with UNSW, we present a modified dial-a-ride problem that incorporates user demand response, and several heuristic algorithms to solve it. Such a model can evaluate different pricing structures (tested zone-based, distance-based, and flat fee structures using data from NYC, and found that a zonal scheme can provide the best revenue and ridership optimization). This can allow planners to design service zones with different pricing structures (including integrated fare bundles with transit in a MaaS environment), integrate pricing with route and destination recommendation, and equity considerations with customized pricing for underserved population segments.

The paper can be found here:
https://www.sciencedirect.com/science/article/pii/S1366554521003562?dgcid=coauthor