Comparison of the elevator traffic flow prediction between the neural networks of CNN and LSTM

Mo  Shi; Yeol  Choi

doi:10.59400/icse1871

Mo Shi School of Architecture, Kyungpook National University, Daegu 41566, Korea; HaXell Elevator Co., Ltd., Shanghai 201801, China
Yeol Choi School of Architecture, Kyungpook National University, Daegu 41566, Korea

Article ID: 1871

13 Views, 5 PDF Downloads

Keywords: ETF (Elevator Traffic Flow); neural networks; CNN (Convolutional Neural Networks); LSTM (Long Short-Term Memory); prediction

Abstract

With urbanization rapidly increasing, the demand for efficient elevator systems is becoming ever more pressing, particularly in crowded urban centers where high-rise buildings are prevalent. To solve this issue, elevator traffic analysis and prediction have emerged as critical components for optimizing elevator control systems. The elevator traffic flow prediction not only ensures smoother operations during peak usage times but also significantly reduces waiting periods for passengers, thereby enhancing overall convenience. By leveraging neural networks, the performance of elevator control systems is expected to be improved, leading to more efficient and convenient elevator utilization in both residential and commercial environments. Over the past few decades, the rapid advancements in neural networks have provided valuable tools for predicting traffic flows. In this research, a total of 655 actual ETF (Elevator Traffic Flow) data points from a typical office building on a weekday are utilized to analyze and predict traffic patterns using CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory). The objective is not only to demonstrate the applicability of the neural networks in predicting elevator traffic flow but also to conduct a comparative analysis to identify which offers greater accuracy and suitability for the elevator traffic flow prediction. By enhancing the capabilities of elevator control systems through CNN or LSTM, this research seeks to improve not only the efficiency of elevator operations but also the overall living and working environment in urban cities. The findings from this research can inform subsequent research efforts, encouraging a deeper exploration of how synthetic predictions can further optimize elevator control systems, while the synthetic elevator control system is expected to lead to significant improvements in passenger experience, reducing wait times and increasing overall satisfaction in both residential and commercial buildings. Therefore, the insights gained from this research are expected to play a crucial role in shaping the future of smart buildings, aligning with the demands of modern urban living.

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