The present disclosure relates to a method and system for determining a load status of a vehicle. The load status of the vehicle indicates whether the vehicle is available to accept an order. The method includes establishing a communication with a mobile terminal associated with the vehicle and a server; obtaining a record of a first prior order corresponding to a first prior transaction associated with the terminal by the server; determining an estimated end time of the first prior transaction by the server; and determining the load status of the vehicle based on a current time and the estimated end time of the first prior transaction by the server.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system, comprising: at least one computer-readable storage medium including a first set of instructions for determining a load status of a vehicle; and at least one processor in communication with the at least one computer-readable storage medium, wherein when executing the first set of instructions, the at least one processor is directed to: send signals to a terminal associated with the vehicle to establish a communication with the terminal; obtain structured data including a record of a first prior order corresponding to a first prior transaction associated with the terminal; determine an estimated end time of the first prior transaction based on the record; and determine the load status of the vehicle based on a current time and the estimated end time of the first prior transaction. 2. The system of claim 1, wherein to obtain the structured data including the record of the first prior order, the at least one processor is further directed to: obtain structured data including records of a plurality of prior orders within a first time period, wherein each of the plurality of prior orders corresponds to one of prior transactions associated with the terminal; for each of the plurality of prior orders, determine a time difference between a broadcasting time of the prior order and the current time; and determine the prior order corresponding to a smallest time difference among the plurality of time differences as the first prior order. 3. The system of claim 1, wherein to determine the load status of the vehicle, the at least one processor is further directed to: determine a confidence value of the estimated end time based on the estimated end time and the current time; determine whether the confidence value is less than a threshold; and determine the vehicle is unavailable to accept an order when the confidence value is equal or greater than the threshold and when the estimated end time is later than the current time. 4. The system of claim 1, wherein to determine the load status of the vehicle, the at least one processor is further directed to: determine a confidence value of the estimated end time based on the estimated end time and the current time; determine whether the confidence value is less than a threshold; and when the confidence value is less than the threshold, determine the load status of the vehicle based on at least one of a plurality of locations of the terminal and a plurality of locations of prior user equipment associated with the first prior order within a second time period, wherein the second time period includes a first set of instances and a second set of instances, each of the plurality of locations of the terminal is associated with one instance of the first set of instances, and each of the plurality of locations of the prior user equipment is associated with one instance of the second set of instances. 5. The system of claim 4, wherein to determine the load status of the vehicle, the at least one processor is further directed to: obtain the plurality of locations of the terminal; obtain map data of a geographical area encompassing the plurality of locations; determine one or more features of the vehicle based on the plurality of locations of the terminal and the map data; and determine the load status of the vehicle based on the features of the vehicle by using a neural network model. 6. The system of claim 5, wherein the features include at least one of: a velocity of the vehicle; an average velocity of vehicles in a vicinity of the location; a maximal velocity of vehicles in the vicinity of the location; a minimal velocity of vehicles in the vicinity of the location; a difference between the velocity of the vehicle and the average velocity; a difference between the velocity of the vehicle and the maximal velocity; a difference between the velocity of the vehicle and the minimal velocity; road sections passed through by the vehicle; a number of the road sections passed through by the vehicle; an average number of road sections passed through by vehicles in the vicinity of the location; and a difference between the number of road sections passed through by the vehicle and the average number of road sections passed through by vehicles in the vicinity of the location. 7. The system of claim 5, wherein to determine the neural network model, the at least one processor is further directed to: obtain a plurality of historical locations of a plurality of example terminals associated with a plurality of example vehicles within a third time period; obtain historical load statuses of the plurality of example vehicles; determine historical features of the plurality of example vehicles based on the historical locations of the example mobile terminals and the map data; and determine the neural network model based on the historical load statuses of the example vehicles and the historical features. 8. The system of claim 4, wherein to determine the load status of the vehicle, the at least one processor is further directed to: obtain the plurality of locations of the terminal; obtain the plurality of locations of the prior user equipment; compare the plurality of locations of the terminal with the plurality of locations of the prior user equipment to determine a distance between the terminal and the prior user equipment; and determine the load status of the vehicle based on the distance between the terminal and the prior user equipment. 9. The system of claim 8, wherein to determine the distance between the terminal and the prior user equipment, the at least one processor is directed to: obtain a reference location of the terminal from the plurality of locations of the terminal associated with a first instance among the first set of instances within the second time period; obtain a reference location of the prior user equipment from the plurality of locations of the prior user equipment associated with a second instance among the second set of instances within the second time period, wherein a difference between the first instance and the second instance is smaller than a predetermined time interval; and determine a distance between the reference location of the terminal and the reference location of the prior user equipment. 10-11. (canceled) 12. The system of claim 1, wherein the storage medium further stores a second set of instructions for broadcasting a current order, wherein when the at least one processor executes the second set of instructions, the at least one processor is further directed to: receive first electronic signals including an order from a current user equipment located at a target location; identify a plurality of candidate terminals within a range around the target location; wherein each of the plurality of candidate terminals corresponds to a candidate vehicle; for the plurality of candidate terminals, execute the first set of instructions to determine the load status of each of the candidate vehicles, wherein the load status of each of the candidate vehicles indicates whether each of the candidate vehicles is available to accept the order; select, based on the load status of each of the candidate vehicles, from the plurality of the candidate terminals at least one target terminal associated with one of the candidate vehicles that is available to accept the order; and generate and send second electronic signals including the order to the at least one selected target terminal. 13. A method for determining a load status of a vehicle implemented on an electronic device having at least one processor and at least one storage medium, the method comprising: sending, by the electronic device, signals to a terminal associated with the vehicle to establish a communication between the terminal associated with the vehicle and the at least one processor of the electronic device; obtaining structured data including a record of a first prior order associated with a first prior transaction associated with the terminal by the electronic device; determining an estimated end time of the first prior transaction by the electronic device; and determining the load status of the vehicle based on a current time and the estimated end time of the first prior transaction by the electronic device. 14. The method of claim 13, wherein obtaining the structured data including the record of the first prior order further includes: obtaining structured data including records of a plurality of prior orders within a first time period by the electronic device, wherein each of the plurality of prior orders corresponds to one of prior transactions associated with the terminal; for each of the plurality of prior orders, determining a time difference between a broadcasting time of the prior order and the current time bar the electronic device; and determining the prior order corresponding to the smallest time difference among the plurality of time differences as the first prior order by the electronic device. 15. The method of claim 13, wherein determining the load status of the vehicle further includes: determining a confidence value of the estimated end time based on the estimated end time and the current time by the electronic device; determining whether the confidence value is less than a threshold by the electronic device; and determining the vehicle is unavailable to accept an order bar the electronic device when the confidence value is equal or greater than the threshold and when the estimated end time is later than the current time. 16. The method of claim 13, wherein determining the load status of the vehicle further includes: determining a confidence value of the estimated end time based on the estimated end time and the current time by the electronic device; determining whether the confidence value is less than a threshold by the electronic device; and when the confidence value is less than the threshold, determining the state of the vehicle based on at least one of a plurality of locations of the terminal and a plurality of locations of a prior user equipment associated with the first prior order within a second time period by the electronic device, wherein the second predetermined time period includes a first set of instances and a second set of instances, each of the plurality of locations of the terminal is associated with one instance of the first set of instances, and each of the plurality of locations of the prior user equipment is associated with one instance of the second set of instances. 17. The method of claim 16, wherein determining the load status of the vehicle further includes: obtaining the plurality of locations of the terminal by the electronic device; obtaining map data of a geographical area encompassing the plurality of locations by the electronic device; determining one or more features of the vehicle based on the plurality of locations of the terminal and the map data by the electronic device; and determining the load status of the vehicle based on the features of the vehicle by using a neural network model by the electronic device. 18. The method of claim 17, wherein the feature include at least one of: a velocity of the vehicle; an average velocity of vehicles in a vicinity of the location; a maximal velocity of vehicles in the vicinity of the location; a minimal velocity of vehicles in the vicinity of the location; a difference between the velocity of the vehicle and the average velocity; a difference between the velocity of the vehicle and the maximal velocity; a difference between the velocity of the vehicle and the minimal velocity; road sections passed through by the vehicle; a number of the road sections passed through by the vehicle; an average number of road sections passed through by vehicles in the vicinity of the location; and a difference between the number of road sections passed through by the vehicle and the average number of road sections passed through by vehicles in the vicinity of the location. 19. The method of claim 17, wherein the neural network model is determined by performing operations including: obtaining a plurality of historical locations of a plurality of example terminals associated with a plurality of example vehicles within a third time period by the electronic device; obtaining historical load statuses of the plurality of example vehicles by the electronic device; determining historical features of the plurality of example vehicles based on the historical locations of the example terminals and the map data by the electronic device; and determining the neural network model based on the historical load statuses of the example vehicles and the historical features by the electronic device. 20. The method of claim 16, wherein determining the load status of the vehicle further includes: obtaining the plurality of locations of the terminal by the electronic device; obtaining the plurality of locations of the prior user equipment by the electronic device; comparing the plurality of locations of the terminal with the plurality of locations of the prior user equipment to determine a distance between the terminal and the prior user equipment by the electronic device; and determining the load status of the vehicle based on the distance between the terminal and the prior user equipment by the electronic device. 21. The method of claim 20, wherein determining the distance between the terminal and the prior user equipment further comprising: obtaining, by the electronic device, a reference location of the terminal from the plurality of locations of the terminal associated with a first instance among the first set of instances within the second time period; obtaining, by the electronic device, a reference location of the prior user equipment from the plurality of locations of the prior user equipment associated with a second instance among the second set of instances within the second time period, wherein a difference between the first instance and the second instance is smaller than a predetermined time interval; and determining a distance between the reference location of the terminal and the reference location of the prior user equipment by the electronic device. 22-23. (canceled) 24. The method of claim 13, further comprising: receiving, by the electronic device, first electronic signals including an order from a current user equipment located at a target location; identifying, by the electronic device, a plurality of candidate terminals within a range around the target location; wherein each of the candidate terminals corresponds to a candidate vehicle; for the plurality of candidate terminals, determining, by the electronic device, the load status of each of the candidate vehicles, wherein the load status of each of the candidate vehicles indicates whether each of the candidate vehicles is available to accept the order; selecting, based on the load status of each of the candidate vehicles, from the plurality of the candidate terminals at least one target terminal associated with one of the candidate vehicles that is available to accept the order; and generating and sending second electronic signals including the order to the at least one selected target terminal by the electronic device.