System and method for ride order dispatching

1. A ride order dispatching system, comprising:

a processor; and

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

obtaining, from a computing device, a current location of a vehicle;

inputting the current location of the vehicle and a time to a trained neural network model to obtain action information for the vehicle, the action information comprising:

staying at the current location of the vehicle, re-positioning the vehicle, or accepting a ride order; and

transmitting the action information to the computing device to cause the vehicle to stay at the current location, re-position to another location, or accept the ride order by proceeding to a pick-up location of the ride order,

wherein the neural network model comprises an input layer for receiving one or more action inputs and one or more state inputs, one or more hidden layers, and an output layer for generating a state-action value output, and

the one or more action inputs comprise a destination and a drop-off time associated with performing a vehicle trip,

the one or more state inputs comprise location information of the vehicle and a pick-up time associated with the vehicle trip,

the state-action value comprises a cumulative reward, and

the input layers, the hidden layers, and the output layer are in a sequence and every two neighbor layers are associated with one or more weights.

2. The system of claim 1 , wherein:

the neural network model comprises a deep neural network and a reinforcement learning algorithm; and

the deep neural network comprises two deep-Q networks.

3. The system of claim 1 , wherein the neural network model is trained by:

modeling the vehicle trips by passing the action inputs and state inputs through the hidden layers to obtain the output, each vehicle trip associated with a first state corresponding to an origin, a second state corresponding to a destination, an action corresponding to fulfilling a ride order from the origin to the destination, and a reward corresponding to fees for the ride; and

updating the weights to maximize the cumulative reward.

4. The system of claim 3 , wherein modeling the vehicle trips comprises:

modeling a first vehicle trip to obtain the second state associated with the first vehicle trip; and

searching from a spatio-temporal space to obtain an action to perform at the second state, the spatio-temporal space is within a spatial threshold from the second state.

5. The system of claim 3 , wherein modeling the vehicle trips comprises:

modeling a first vehicle trip to obtain the second state associated with the first vehicle trip; and

searching from a spatio-temporal space to obtain an action to perform at the second state, the spatio-temporal space is within a temporal threshold from the second state.

6. The system of claim 1 , wherein:

data used for training the neural network model comprises (1) a plurality of the vehicle trips each associated with a measurement period, and (2) one or more terminal states each associated with an end of the measurement period.

7. A computer-implemented method for ride order dispatching, comprising:

obtaining, from a computing device, a current location of a vehicle;

inputting the current location of the vehicle and a time to a trained neural network model to obtain action information for the vehicle, the action information

comprising: staying at the current location of the vehicle, re-positioning the vehicle, or accepting a ride order; and

transmitting the action information to the computing device to cause the vehicle to stay at the current location, re-position to another location, or accept the ride order by proceeding to a pick-up location of the ride order,

wherein the neural network model comprises an input layer for receiving one or more action inputs and one or more state inputs, one or more hidden layers, and an output layer for generating a state-action value output, and

the one or more action inputs comprise a destination and a drop-off time associated with performing a vehicle trip,

the one or more state inputs comprise location information of the vehicle and a pick-up time associated with the vehicle trip,

the state-action value comprises a cumulative reward, and

the input layers, the hidden layers, and the output layer are in a sequence and every two neighbor layers are associated with one or more weights.

8. The method of claim 7 , wherein:

the neural network model comprises a deep neural network and a reinforcement learning algorithm; and

the deep neural network comprises two deep-Q networks.

9. The method of claim 7 , wherein the neural network model is trained by:

modeling the vehicle trips by passing the action inputs and state inputs through the hidden layers to obtain the output, each vehicle trip associated with a first state corresponding to an origin, a second state corresponding to a destination, an action corresponding to fulfilling a ride order from the origin to the destination, and a reward corresponding to fees for the ride; and

updating the weights to maximize the cumulative reward.

10. The method of claim 9 , wherein modeling the vehicle trips comprises:

modeling a first vehicle trip to obtain the second state associated with the first vehicle trip; and

searching from a spatio-temporal space to obtain an action to perform at the second state, the spatio-temporal space is within a spatial threshold from the second state.

11. The method of claim 9 , wherein modeling the vehicle trips comprises:

modeling a first vehicle trip to obtain the second state associated with the first vehicle trip; and

searching from a spatio-temporal space to obtain an action to perform at the second state, the spatio-temporal space is within a temporal threshold from the second state.

12. The method of claim 7 , wherein:

data used for training the neural network model comprises (1) a plurality of the vehicle trips each associated with a measurement period, and (2) the data used for training the neural network model comprises one or more terminal states each associated with an end of the measurement period.

13. A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform a ride order dispatching method, the method comprising:

obtaining, from a computing device, a current location of a vehicle;

inputting the current location of the vehicle and a time to a trained neural network model to obtain action information for the vehicle, the action information comprising: staying at the current location of the vehicle, re-positioning the vehicle,

or accepting a ride order; and

transmitting the action information to the computing device to cause the vehicle to stay at the current location, re-position to another location, or accept the ride order by proceeding to a pick-up location of the ride order,

wherein the neural network model comprises an input layer for receiving one or more action inputs and one or more state inputs, one or more hidden layers, and an output layer for generating a state-action value output, and

the one or more action inputs comprise a destination and a drop-off time associated with performing a vehicle trip,

the one or more state inputs comprise location information of the vehicle and a pick-up time associated with the vehicle trip,

the state-action value comprises a cumulative reward, and

the input layers, the hidden layers, and the output layer are in a sequence and every two neighbor layers are associated with one or more weights.

14. The storage medium of claim 13 , wherein the neural network model is trained by:

modeling the vehicle trips by passing the action inputs and state inputs through the hidden layers to obtain the output, each vehicle trip associated with a first state corresponding to an origin, a second state corresponding to a destination, an action corresponding to fulfilling a ride order from the origin to the destination, and a reward corresponding to fees for the ride; and

updating the weights to maximize the cumulative reward.