A method and system for controlling elevator dispatch is provided. User data, including user behavior, is collected from a number of users over a specified time period. Elevator use data for a number of elevators in a building is also collected over the specified time period. Applying the user data and elevator use data, an elevator dispatch model is constructed that predicts future elevator use according to predicted user needs. An elevator control system dispatches the elevators according to the dispatch model. The elevator dispatch model is refined according to feedback data collected from users over a subsequent time period.

In an approach for determining an optimal path for an elevator, a processor receives information, wherein the information includes social media data associated with a user and calendar entries associated with the user, indicating one or more locations within a building. A processor analyzes the received information. A processor determines a location to send an elevator within the building to minimize movement of the elevator, based on the analyzed information. A processor causes the elevator to move to the location.

According to an aspect, there is provided a method for forecasting elevator passenger traffic of an elevator group. The method comprises training a statistical traffic model describing a traffic profile for a specific cycle with historical timestamped origin-destination passenger counts, obtaining timestamped origin-destination passenger counts for a current cycle, generating an elevator passenger traffic forecast based on the trained statistical traffic model and the timestamped origin-destination passenger counts for the current cycle, and outputting the elevator passenger traffic forecast for use by an elevator group control.

A method and an apparatus for computing allocation decisions in an elevator system is provided. Historical passenger batch journey data relating to the elevator system is obtained, wherein each passenger batch journey includes an origin and a destination floor of the journey, the number of passengers of the journey and the time of the journey. Historical passenger traffic statistics are constructed based on the passenger batch journey data, and expected calls are modelled based on the passenger traffic statistics. The modelled expected call is taken into account in computing subsequent allocation decisions in the elevator system.

Elevator groups are known have very high peak power demand. The peak power capacity of the power line can be reduced by using a power arrangement involving a battery. The battery can be discharged for operating elevators when the power demand exceeds the capacity of the power line. The battery may be further used for reducing power cost by charging the battery during lower cost time.

An elevator control apparatus for shortening the sum of the waiting times of waiting people waiting for elevators on all floors includes an analysis unit configured to analyze each of images obtained by capturing elevator boarding/exiting portions on floors where an elevator car can stop, and to recognize a person in the image, an acquisition unit configured to, based on a result of the analysis by the analysis unit, acquire an elevator usage history of the person, and a generation unit configured to, based on the elevator usage history acquired by the acquisition unit, generate an operation schedule for the elevators.

In an approach for determining an optimal path for an elevator, a processor receives information, wherein the information includes social media data associated with a user and calendar entries associated with the user, indicating one or more locations within a building. A processor analyzes the received information. A processor determines a location to send an elevator within the building to minimize movement of the elevator, based on the analyzed information. A processor causes the elevator to move to the location.

In an approach for determining an optimal path for an elevator, a processor receives information, wherein the information includes social media data associated with a user and calendar entries associated with the user, indicating one or more locations within a building. A processor analyzes the received information. A processor determines a location to send an elevator within the building to minimize movement of the elevator, based on the analyzed information. A processor causes the elevator to move to the location.

The present disclosure provides a system and a method for controlling motion of a bank of elevators. The method includes accepting current requests for service by the bank of elevators, accepting a partial trajectory of a motion of a person moving in an environment serviced by the bank of elevators, and obtaining a probability of a future elevator request. The method further includes processing the partial trajectory with a neural network trained to estimate a weighted combination of probability density functions that indicates an arrival time distribution of the person, and generating a set of possible future requests jointly representing the probability of the future elevator request and the arrival time distribution. The method further includes optimizing a schedule of the bank of elevators to serve the current requests and the set of possible future requests, and controlling the bank of elevators according to the schedule.

According to an aspect, there is provided a method and an apparatus for determining an allocation decision for at least one elevator. In the solution existing calls are used in an elevator system as a first input in a machine learning module. The first input is processed with the machine learning module to provide a first output comprising a first allocation decision. The first output is then used as a second input in an iterative module. The second input is processed with the iterative module to provide a second output comprising a second allocation decision. The second allocation decision is provided to an elevator control module and to an allocation decision storage for further machine learning module training.