BACKGROUND

Elevator group control systems may use passenger traffic forecasts to avoid bad dispatching decisions in constantly changing traffic conditions. Forecasting future traffic can be implemented, for example, by using long-term historical traffic statistics. However, the long-term historical traffic statistics often fail to describe unexpected changes in the passenger traffic. This means, for example, that when unexpected changes occur, the long-term historical statistics may result in incorrect forecasts of the traffic. This can result in deterioration of elevator group performance.

Thus, it would be beneficial to have a solution that would alleviate at least one of these drawbacks.

SUMMARY

According to at least some of the aspects, a solution is provided that produces a forecast that is able to adapt to unexpected changes by combining historical traffic information and information based on the traffic observed during a current cycle of time, for example, a current day.

According to a first aspect of the invention, 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 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.

In an embodiment, the timestamped origin-destination counts for the current cycle comprise origin-destination counts collected during the current day.

In an embodiment, additionally or alternatively, the method further comprises retraining the statistical traffic model with the time-stamped origin-destination counts of the current cycle.

In an embodiment, additionally or alternatively, the method further comprises further comprising filtering the origin-destination passenger counts for the current cycle using a Bayesian filtering algorithm.

In an embodiment, additionally or alternatively, the method further comprises estimating the origin-destination passenger counts from at least one of traffic measurements, landing calls, destination calls, car calls, and boarding and alighting passenger counts.

In an embodiment, additionally or alternatively, the method further comprises determining boarding and alighting passenger counts for estimating the origin-destination passenger counts with at least one of a load weighing device, a curtain of light, and a stereo camera installed inside the elevator car.

In an embodiment, additionally or alternatively, the elevator passenger traffic forecast comprises at least one of an origin-destination arrival rate, an incoming arrival rate, an interfloor arrival rate, and an outgoing arrival rate, and an arrival rate from a specific set of origin floors to a specific set of destination floors. The term “specific set” may refer to one or more origin floors and/or destination floors.

According to a second aspect of the invention, there is provided an apparatus for forecasting elevator passenger traffic of an elevator group. The apparatus comprises means for using historical timestamped origin-destination passenger counts to train a statistical traffic model describing a traffic profile for a specific cycle; means for obtaining timestamped origin-destination passenger counts for a current cycle; means for 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 means for outputting the elevator passenger traffic forecast for use by an elevator group control.

In an embodiment, the apparatus further comprises means for implementing any of the embodiments of the method of the first aspect.

According to a third aspect of the invention, there is provided an elevator system comprising an apparatus according to the second aspect.

According to a fourth aspect of the invention, there is provided a computer program comprising program code, which when executed by at least one processing unit, causes the at least one processing unit to perform the method of the first aspect.

In an embodiment, the computer program is embodied on a computer readable medium.

According to a fifth aspect, there is provided an apparatus for forecasting elevator passenger traffic of an elevator group. The apparatus comprises at least one processing unit and at least one memory. The at least one memory stores program instructions that, when executed by the at least one processing unit, cause the apparatus to train a statistical traffic model describing a traffic profile for a specific cycle with historical timestamped origin-destination passenger counts; obtain timestamped origin-destination passenger counts for a current cycle; generate an elevator passenger traffic forecast based on the trained statistical traffic model and the timestamped origin-destination passenger counts for the current cycle; and to output the elevator passenger traffic forecast for use by an elevator group control.

In an embodiment, the timestamped origin-destination counts for the current cycle comprise origin-destination counts collected during a current day.

In an embodiment, additionally or alternatively, the at least one memory stores program instructions that, when executed by the at least one processing unit, cause the apparatus to retrain the statistical traffic model with the time-stamped origin-destination counts of the current cycle.

In an embodiment, additionally or alternatively, the at least one memory stores program instructions that, when executed by the at least one processing unit, cause the apparatus to filter the origin-destination passenger counts for the current cycle using a Bayesian filtering algorithm.

In an embodiment, additionally or alternatively, the at least one memory stores program instructions that, when executed by the at least one processing unit, cause the apparatus to estimate the origin-destination passenger counts from at least one of traffic measurements, landing calls, destination calls, car calls, and boarding and alighting passenger counts.

In an embodiment, additionally or alternatively, the at least one memory stores program instructions that, when executed by the at least one processing unit, cause the apparatus to determine boarding and alighting passenger counts for estimating the origin-destination passenger counts with at least one of a load weighing device, a curtain of light, and a stereo camera installed inside the elevator car.

In an embodiment, additionally or alternatively, the elevator passenger traffic forecast comprises at least one of an origin-destination arrival rate, an incoming arrival rate, an interfloor arrival rate, and an outgoing arrival rate.

The above discussed means may be implemented, for example, using at least one processor, at least one processor and at least one memory connected to the at least one processor, or at least one processor, at least one memory connected to the at least one processor and an input/output interface connected to the at least one processor.

