System and method for ride order dispatching and vehicle repositioning

A system for ride order dispatching and vehicle repositioning, comprising a processor and a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, cause the system to perform operations comprising:

• obtaining information comprising a location of a vehicle, current orders, and a current time;
• inputting the obtained information to a trained model of a model;
• and
• determining action information for the vehicle based on an output of the trained model, the action information comprising:
• re-positioning the vehicle or accepting a ride order, wherein:
• the model comprises a single-driver deep-Q network (SD-DQN) and is configured with model instructions for performing:
• receiving information of drivers and information of orders as inputs;
• obtaining a global state based on the information of drivers, the information of orders, and a global time, each state transition of the global state being from a single driver completing a trip to the single driver completing a next trip;
• querying a plurality of driver-order pairs and driver-reposition pairs based at least on the obtained global state to determine a Q-value of the SD-DQN for the single driver;
• determining the action information as the output based at least on the determined Q-value to optimize a return for the single driver;
• and
• controlling the vehicle to execute the determined action information.

A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform operations comprising:

• obtaining information comprising a location of a vehicle, current orders, and a current time;
• inputting the obtained information to a trained model of a model;
• and
• determining action information for the vehicle based on an output of the trained model, the action information comprising:
• re-positioning the vehicle or accepting a ride order, wherein:
• the model comprises a single-driver deep-Q network (SD-DQN) and is configured with model instructions for performing:
• receiving information of drivers and information of orders as inputs;
• obtaining a global state based on the information of drivers, the information of orders, and a global time, each state transition of the global state being from a single driver completing a trip to the single driver completing a next trip;
• querying a plurality of driver-order pairs and driver-reposition pairs based at least on the obtained global state to determine a Q-value of the SD-DQN for the single driver;
• determining the action information as the output based at least on the determined Q-value to optimize a return for the single driver;
• and
• controlling the vehicle to execute the determined action information.