Systems and methods for repositioning vehicles in a ride-hailing platform

A system comprising one or more processors and one or more non-transitory computer-readable memories coupled to the one or more processors, the one or more non-transitory computer-readable memories storing instructions that, when executed by the one or more processors, cause the system to perform operations comprising:

• obtaining a plurality of current features associated with a vehicle located in one of the plurality of grid cells;
• inputting the plurality current features associated with the vehicle into a neural network, wherein the neural network is trained using a state-action-reward-state-action (SARSA) framework based on a plurality of historical trajectories of one or more historical vehicles, historical supply-demand status in a plurality of neighboring grid cells of the one or more historical vehicles, and a plurality of actual conditional action values learned from historical data;
• obtaining, from the neural network, a plurality of conditional action values for repositioning the vehicle to a plurality of target grid cells conditioned upon the plurality current features associated with the vehicle, wherein the plurality of target grid cells comprise the one grid cell that the vehicle is currently located in and other grid cells in the plurality of grid cells that are within two or more layers surrounding the one grid cell;
• and
• sending one or more of the plurality of target grid cells with highest conditional action values to the vehicle for repositioning.

A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• obtaining a plurality of current features associated with a vehicle located in one of the plurality of grid cells representing a region;
• inputting the plurality current features associated with the vehicle into a neural network, wherein the neural network is trained using a state-action-reward-state-action (SARSA) framework based on a plurality of historical trajectories of one or more historical vehicles, historical supply-demand status in a plurality of neighboring grid cells of the one or more historical vehicles, and a plurality of actual conditional action values learned from historical data;
• obtaining, from the neural network, a plurality of conditional action values for repositioning the vehicle to a plurality of target grid cells conditioned upon the plurality current features associated with the vehicle, wherein the plurality of target grid cells comprise the one grid cell that the vehicle is currently located in and other grid cells in the plurality of grid cells that are within two or more layers surrounding the one grid cell;
• and
• sending one or more of the plurality of target grid cells with highest conditional action values to the vehicle for repositioning.