A benchmark and real-time estimator for the passenger arrival rate to elevator system

摘要

Arrival rate is the number of passengers arriving for elevator service in a certain period of time. Arrival rate is fundamental in expressing the heaviness of the traffic. Hence, it is vital for determining the required number of elevators and the specifications of each elevator such as the speed, capacity, and sector sizes. The passenger arrival process is a random process that is full of noise, and a processing step is required to extract the arrival rate from recorded arrival times of passengers. This work develops a real-time estimator and a benchmark for estimating the arrival rate. There are three contributions in this work; the first is suggesting a benchmark for estimating arrival rate; singular spectrum analysis extracts the arrival rate from noisy data. Hence, singular spectrum analysis is suggested as a benchmark for evaluating the performance of other algorithms. Even though singular spectrum analysis is powerful in extracting the arrival rate, it is not convenient for updating the arrival rate in real time. The second contribution is developing a real-time estimator for the passenger arrival rate that updates its parameters dynamically; dynamic exponentially weighted moving average was developed to provide instantaneous arrival rate updates. The third contribution is introducing exponentially weighted moving average as a linear model for passenger arrival, which opens the door to a large number of model-based algorithms in control theory; Kalman filtering was developed in this work on the top of the EWMA linear model. The results of applying Kalman filtering and DEWMA to real-life data show them as efficient methods for estimating passenger arrival rate to the elevators in real time.