Abstract: For elevator car dispatching scenarios, the standard remaining response time (RRT) estimation is augmented by a selected amount of time where a horizon floor is to be served before reaching the hall call to be assigned. The augmentation may be made by adding a fixed RRT penalty, by assuming a car call stop after the horizon floor at a selected floor, or by using a neural network.

Abstract: A method for training a neural network used to estimate for an elevator the remaining response time for the elevator to service a hall call. The training, which results in adjusting connection weights between nodes of the neural network, is performed while the elevator is in actual operation. The method is not restricted to any particular architecture of neural network. The method uses a cutoff to limit changes to the connection weights, and provides for scaling the different inputs to the neural network so that all inputs lie in a predetermined range. The method also provides for training in case the elevator is diverted from servicing the hall call by an intervening hall call.

Abstract: A method for adapting to observed special use patterns a neural network used to estimate quantities needed by an elevator dispatching system responsible for assigning the elevator or another elevator to a hall call. Rather than simply refining values of existing connection weights to train the neural network to provide acceptable outputs for predetermined inputs, the method analyzes use information to determine whether additional inputs to the neural network might be advantageous and what those inputs might be. If so, the method alters the neural network architecture by providing new input nodes and corresponding connection weights, the connection weights having initially relatively small values. All connection weights can then be adjusted during actual operation of the elevator to accommodate the new input nodes.

Abstract: A remaining response time for an elevator car under consideration for assignment to a newly registered hall call is estimated by using a neural network. The neural network or any other downstream module may be standardized for use in any building by use of an upstream fixed length stop description that summarizes the state of the building at the time of the registration of the new hall call for one or more postulated paths of each and every car under consideration for answering the new hall call.