We analyze the equilibrium behavior of a large population of self-interested drivers, such as taxi drivers or those operating on a ride-hailing platform. Each driver is modeled as a Markov Decision Process (MDP), aiming to maximize their long-run average reward by strategically selecting repositioning actions. The interaction among drivers arises through shared resource constraints, leading to the model of fluid queues. The resulting equilibria can be characterized as solutions to a convex program, which is an extension of the classical Eisenberg-Gale program where waiting times play a role analogous to prices. To accurately predict actual mobility patterns, the reward function in the MDP model is inferred using an inverse reinforcement learning (IRL) technique in the data. We adopt a linear reward structure, and the features in the reward function are constructed by employing sparse principal component analysis (SPCA) and symbolic regression. These methods provide explicit linear and non-linear combinations of raw features.