Estimating the elevator traffic system arrival rate using exponentially weighted moving average(EWMA)

Hammoudeh, Ahmad; Al-Sharif, Lutfi

doi:10.1109/aeect.2017.8257757

Cited by 7 publications

(6 citation statements)

References 3 publications

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“…A block diagram for EWMA estimator is shown in Figure 3. In the following part of this section, some results from Hammoudeh and Al-Sharif 13 are highlighted. In the next section, SSA is proposed as an efficient mathematical tool for extracting the arrival rate information from noisy data.…”

Section: Exponential Smoothingmentioning

confidence: 99%

“…Different methods for estimating the elevator traffic system arrival rate are studied in Hammoudeh and Al-Sharif. 13 It was shown that EWMA outperforms simple moving average (SMA). Moreover, a graph for estimating the optimum value of the smoothing constant was obtained by simulation under a predefined profile of the arrival rate.…”

Section: Exponential Smoothingmentioning

confidence: 99%

“…However, the correct arrival rate typically is not available. In Hammoudeh and Al-Sharif, 13 this problem was overcome by generating the arrival times under a predefined model of the arrival rate. This model simply represents the correct arrival rate.…”

Section: Ssamentioning

confidence: 99%

“…This performs very well when the curve describing the passenger arrival rate is known, but it does not perform well when evaluating the estimator under reallife data. Another workaround in Hammoudeh and Al-Sharif, 13 an estimated arrival rate for real data was assessed by comparing an estimate (EWMA) to another estimate (SMA). In this section, SSA as a non-parametric method is suggested as a benchmark to evaluate other parametric estimates.…”

Section: Ssamentioning

confidence: 99%

“…Smoothing factor formula is shown in equations (12) and (13) Smoothing factor has an upper bound max and a lower bound min .…”

Section: Exponential Smoothing With Dynamic Smoothing Factormentioning

confidence: 99%

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

Hammoudeh

Al-Sharif

Al-Shabi

2018

Building Services Engineering Research and Technology

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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. Practical application: The methods presented in this paper would allow an elevator controller designer to detect the intensity of the passenger arrival rate. By doing this, it is possible for the elevator controller to switch between different group control algorithms. For example, it could decide to switch from conventional group control to sectoring control and vice versa.

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Section: Exponential Smoothingmentioning

confidence: 99%

Section: Exponential Smoothingmentioning

confidence: 99%

Section: Ssamentioning

confidence: 99%

Section: Ssamentioning

confidence: 99%

“…Smoothing factor formula is shown in equations (12) and (13) Smoothing factor has an upper bound max and a lower bound min .…”

Section: Exponential Smoothing With Dynamic Smoothing Factormentioning

confidence: 99%