Abstract: Aspects concern a method for controlling vehicles to perform transport tasks comprising supplying information about vehicles and information about transport tasks to a graph neural network by associating each vehicle with a vehicle graph node and each transport task with a transport task graph node, processing the vehicle and the transport graph by the neural network, wherein the neural network determines a feature for each graph node, determining, for each pair of a transport graph node and vehicle graph node, a weight representing a similarity between the features determined for the transport graph node and the vehicle graph node, selecting an assignment between the transport graph nodes and the vehicle graph nodes from a set of possible assignments, wherein the selected assignment maximizes the sum of the weights of the pairs and controlling each vehicle according to the selected assignment.

Abstract: A system for predicting a destination location may include one or more processors and a memory having instructions stored therein. The one or more processors may use at least one recurrent neural network to: process spatial data which may include a first set of information about origin locations and destination locations; process temporal data which may include a second set of information about times at the origin locations and the destination locations; determine hidden state data based on the spatial data and the temporal data, wherein the hidden state data may include data on origin-destination relationships; receive a current input data from a user, wherein the current input data may include an identity of the user and the current origin location of the user; and predict the destination location based on the hidden state data and the current input data.