"Artificial intelligence" based crowd sensing system for elevator car assignment

Abstract

Abstract An elevator control system employing a micro-processor-based group controller (FIG. 2), which communicates with the cars (3, 4) of the system to determine the conditions of the cars, and responds to hall calls registered at a plurality of landings in the building serviced by the cars under control of the group controller, assigning hall calls to cars based on the summation for each car, relative to each call, a weighted summation of a plurality of system response factors, some indicative, and some not, of conditions of the car irrespective of the call being assigned, assigning varying "bonuses" and "penalties" to them in the weighted summation. "Artificial intelligence" techniques are used to predict traffic levels and any crowd build up at various floors to better assign one or more cars to the "crowd" predicted floors, either parking them there, if they were empty, or more appropriately assigning car(s) to the hall calls. Traffic levels at various floors are predicted by collecting passengers and car stop counts in real time and using real time and historic prediction for the traffic levels, with single exponential smoothing and/or linear exponential smoothing. Predicted passenger arrival counts are used to predict any crowd at fifteen second intervals at floors where significant traffic is predicted. Crowd prediction is then adjusted for any hall call stops made and the number of passengers picked up by the cars. The crowd dynamics are matched to car assignment, with one or more cars being sent to crowded floor(s).

Inventors

• 👤 Kandasamy Thanagavelu

Bibliographic Details