FIELD OF THE INVENTION

This disclosure generally relates to methods and devices for ride order dispatching.

BACKGROUND

A vehicle dispatch platform can automatically allocate transportation requests to corresponding vehicles for providing transportation services. Each vehicle driver provides and is rewarded for the transportation service. However, it has been challenging to determine an efficient allocation method that can maximize the gain for each vehicle driver.

SUMMARY

Various embodiments of the present disclosure include systems, methods, and non-transitory computer readable media for ride order dispatching.

According to one aspect, a ride order dispatching system comprises 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. The method comprises: 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.

In some embodiments, the neural network model comprises a deep neural network and a reinforcement learning algorithm, and the deep neural network comprises two deep-Q networks.

In some embodiments, the neural network model comprises: an input layer comprising one or more action inputs and one or more state inputs, one or more hidden layers, and an output layer comprising a state-action value output. The action comprises a destination and a drop-off time associated with performing a vehicle trip. The state comprises geo-coordinates of the vehicle and a pick-up time associated with the vehicle trip. The state-action value comprises a cumulative reward. 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.

In some embodiments, 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.

In some embodiments, 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.

In some embodiments, 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.

In some embodiments, 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.

According to another aspect, a computer-implemented method for ride order dispatching comprises: 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.

According to another aspect, a non-transitory computer-readable storage medium stores instructions that, when executed by a processor, cause the processor to perform a ride order dispatching method. The method comprises: 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.

According to another aspect, a method for ride order dispatching may comprise inputting information on a location of a vehicle and a time to a trained neural network model; and based on a policy generated from the trained neural network model, obtaining action information for the vehicle, the action information comprising: staying at a current position of the vehicle, re-positioning the vehicle, or accepting a ride order. In some embodiments, a non-transitory computer readable media may couple to a processor and comprise instructions that, when executed by the processor, cause the processor to preform various method described herein. The instructions may comprise the trained neural network model and the policy (e.g., the trained neural network model and the policy may be associated with memory locations in the media).

These and other features of the systems, methods, and non-transitory computer readable media disclosed herein, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for purposes of illustration and description only and are not intended as a definition of the limits of the invention.

