A computer-implemented method of route planning for package pickup and delivery, comprising: receiving predetermined locations in a geographic region and data representing a predetermined route connecting the predetermined locations; determining, using at least one processor, unit areas in the geographic region based on sequential nearness of the predetermined locations along the predetermined route, wherein the unit areas comprise a first unit area and a second unit area, and the unit areas are configured such that all locations in the first unit area are to be visited before visiting locations of the second unit area; determining, using the at least one processor, for each of the unit areas, a vector representation of the unit area by applying a graph embedding technique to addresses of locations in the unit area; determining, using the at least one processor, training delivery patterns for unit areas covering the predetermined locations, wherein each training delivery pattern comprises at least one of the unit areas ordered in a visiting sequence; determining, using the at least one processor, a vector representation of each of the training delivery patterns by adding vector representations of the at least one of the unit areas in the training delivery pattern; determining, using the at least one processor, a score for each of the training delivery patterns by inputting the vector representation of each of the training delivery patterns into a first machine learning model, wherein the score indicates an efficiency level for visiting the at least one of the unit areas along the visiting sequence; updating, using the at least one processor, parameters of the first machine learning model based on a determination that the visiting sequence is inconsistent with the predetermined route; determining, using the at least one processor, the first machine learning model for generating the delivery patterns based on a determination that the visiting sequences of all of the training delivery patterns are consistent with the predetermined route; generating, using the at least one processor, delivery patterns for determining a route connecting at least one of the unit areas, wherein each delivery pattern comprises at least one of the unit areas associated with a visiting sequence; in response to receiving task data comprising target locations to visit, determining a target route for visiting the target locations using the delivery patterns and the target data; and sending the target route to a mobile apparatus. receiving predetermined locations in a geographic region and data representing a predetermined route connecting the predetermined locations; determining, using at least one processor, unit areas in the geographic region based on sequential nearness of the predetermined locations along the predetermined route, wherein the unit areas comprise a first unit area and a second unit area, and the unit areas are configured such that all locations in the first unit area are to be visited before visiting locations of the second unit area; determining, using the at least one processor, for each of the unit areas, a vector representation of the unit area by applying a graph embedding technique to addresses of locations in the unit area; determining, using the at least one processor, training delivery patterns for unit areas covering the predetermined locations, wherein each training delivery pattern comprises at least one of the unit areas ordered in a visiting sequence; determining, using the at least one processor, a vector representation of each of the training delivery patterns by adding vector representations of the at least one of the unit areas in the training delivery pattern; determining, using the at least one processor, a score for each of the training delivery patterns by inputting the vector representation of each of the training delivery patterns into a first machine learning model, wherein the score indicates an efficiency level for visiting the at least one of the unit areas along the visiting sequence; updating, using the at least one processor, parameters of the first machine learning model based on a determination that the visiting sequence is inconsistent with the predetermined route; determining, using the at least one processor, the first machine learning model for generating the delivery patterns based on a determination that the visiting sequences of all of the training delivery patterns are consistent with the predetermined route; generating, using the at least one processor, delivery patterns for determining a route connecting at least one of the unit areas, wherein each delivery pattern comprises at least one of the unit areas associated with a visiting sequence; in response to receiving task data comprising target locations to visit, determining a target route for visiting the target locations using the delivery patterns and the target data; and sending the target route to a mobile apparatus.