Abstract: A method for operating a plurality of vehicles is disclosed. The method comprises receiving a first set of vehicle data and a second set of vehicle data, the vehicle data comprising information about each vehicle of the plurality of vehicles, each vehicle operating along at least one fixed route, receiving a first set of environmental data and a second set of environmental data, the environmental data comprising information about each fixed route, and estimating, by means of the global self-learning model and each local-self learning model, a schedule parameter for each vehicle of the plurality of vehicles based on the received first set of vehicle data, the received first set of environmental data, the received second set of vehicle data, the received second set of environmental data, and a predefined interaction model between the global self-learning model and each local-self learning model.

Abstract: The invention relates to a method of controlling a plurality of vehicles, performing the same mission cycle, comprising mapping a first set of planned degrees of progress (CCP1) to the cycle, controlling the vehicles to start the cycle at respective different points in time, determining deviations of the vehicles from a respective planned degree of progress (CCP1i) of the first set of planned degrees of progress (CCP1), mapping, based on the determined deviations, a second set of planned degrees of progress (CCP2) to the cycle, and controlling the vehicles so as to minimize deviations of the vehicles from a respective planned degree of progress (CCP2i) of the second set of planned degrees of progress (CCP2).

Abstract: A method for operating a plurality of vehicles is disclosed. The method comprises receiving (101) a first set of vehicle data and a second set of vehicle data, the vehicle data comprising information about each vehicle of the plurality of vehicles, each vehicle operating along at least one fixed route, receiving (102) a first set of environmental data and a second set of environmental data, the environmental data comprising information about each fixed route, and estimating (103), by means of the global self-learning model and each local-self learning model, a schedule parameter for each vehicle of the plurality of vehicles based on the received first set of vehicle data, the received first set of environmental data, the received second set of vehicle data, the received second set of environmental data, and a predefined interaction model between the global self-learning model and each local-self learning model.

Abstract: A system for calculating a desired time of arrival to a target destination for a vehicle includes a device mountable to a first vehicle. The device includes a transmitter, a receiver, and a logic unit configured to receive signals for determining a current position and a current vehicle speed of the first vehicle, the logic unit being further configured to determine an estimated time of arrival at a target destination for the first vehicle based on predetermined route information, the current position of the first vehicle and the target destination of the first vehicle.