Effectively Relocating Ride-Hailing Drivers Using A Markov Decision Process with Dynamic Sharding

A good relocation strategy for drivers engaging in ride-hailing services is essential to meet demand as and where it arises. Recent work has modelled this as a Markov Decision Process (MDP) to improve driver utilization and income. However, this does not take into account all factors that affect the successfulness of a relocation strategy. In this paper we propose a two-stage relocation strategy, where we first dynamically compute the ideal size of the area to be recommended by considering demand density before the MDP solves for the best movement. We account for competition from other drivers and select a series of representative POIs from the optimal destination area to guide the driver. Simulations using real-world data from cities with different market conditions show that drivers' expected income can be improved by up to 27%.