Systems and methods for optimizing an online on-demand service

A method for determining and optimizing an allocation strategy for an online on-demand transportation service implemented on a computing device having at least one processor, at least one storage medium, and a communication platform connected to a network, comprising:

• storing, into the at least one storage medium, a plurality of historical orders collected from a plurality of passenger terminals and a plurality of driver terminals;
• obtain order information associated with the plurality of historical orders by accessing the at least one storage medium;
• obtaining driver information associated with a plurality of historical drivers corresponding to the plurality of historical orders by accessing the at least one storage medium;
• for each historical driver during a predetermined period of time, determining a plurality of records based on the order information and the driver information according to a decision-making processes model, wherein each record includes a driver's space-time status, a driver's action, a driver's revenue, and a driver's subsequent space-time status, and the decision-making processes model is a Markov Decision Process (MDP) model, wherein the order information and the driver information are inputs of the MDP model, and the outputs of the MDP model are the plurality of records;
• determining a value function based on the plurality of records of each historical driver according to a reinforcement learning model, wherein the plurality of records of each historical driver are inputs of the reinforcement learning model, and the output of the reinforcement learning model is the value function;
• and
• utilizing the value function to determine an allocation strategy for the online on-demand transportation service.

A non-transitory computer readable medium, comprising at least one set of instructions for determining and optimizing an allocation strategy for an online on-demand transportation service, wherein when executing the set of instructions, the at least one set of instructions directs the at least one processor to:

• store, into at least one storage medium, a plurality of historical orders collected from a plurality of passenger terminals and a plurality of driver terminals;
• obtain order information associated with the plurality of historical orders by accessing the at least one storage medium;
• obtain driver information associated with a plurality of historical drivers corresponding to the plurality of historical orders by accessing the at least one storage medium;
• for each historical driver during a predetermined period of time, determine a plurality of records based on the order information and the driver information according to a decision-making processes model, wherein each record includes a driver's space-time status, a driver's action, a driver's revenue, and a driver's subsequent space-time status, and the decision-making processes model is a Markov Decision Process (MDP) model, wherein the order information and the driver information are inputs of the MDP model, and the outputs of the MDP model are the plurality of records;
• determine a value function based on the plurality of records of each historical driver according to a reinforcement learning model, wherein the plurality of records of each historical driver are inputs of the reinforcement learning model, and the output of the reinforcement learning model is the value function;
• and
• utilize the value function to determine an allocation strategy for the online on-demand transportation service.