The Effect of Randomization on Constraint Based Estimation of Elevator Trip Origin-Destination Matrices

We present a constraint programming formulation for the elevator trip origin-destination matrix estimation problem, and study different deterministic and randomized algorithms to solve the problem. An elevator trip consists of successive stops in one direction of travel with passengers inside the elevator. It can be defined as a directed network, where the nodes correspond to the stops on the trip, and the arcs to the possible origins and destinations of the passengers. The goal is to estimate the count of passengers for the origin-destination pairs of every elevator trip occurring in a building. These counts can be used to make passenger traffic forecasts which, in turn, can be used in elevator dispatching to reduce uncertainties related to future passengers. The results show that randomized search improves the quality of estimation results. In addition, the proposed approach satisfies real time elevator group control requirements for estimating elevator trip origin-destination matrices.