We learn how to price in ride-pooling (UberPool) while taking into account the matchings the pricing system induces.
We create a visualization to allow various stakeholders to evaluate the tradeoffs associated with multi-party fairness in the Ride-Pooling (Uber) ecosystem.
We evaluate the efficacy of two different strategies of enforcing fairness in ride-pooling.
We add future information to ride-pooling assignments by using a novel extension to Approximate Dynamic Programming.
A blog post about the joyride my paper 'Neural Approximate Dynamic Programming for On-Demand Ride-Pooling' took me on.