Gaussian Analysis of the Elevator Traffic under the Typical Office Building

Abstract

Elevators serve as indispensable transportation systems in contemporary buildings, facilitating vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated traffic congestion issues within elevator systems. A significant number of elevator passengers voice dissatisfaction with prolonged wait times, leading to impatience and frustration. Traditional approaches to address elevator traffic problems include installing additional elevators or implementing group control systems. However, these solutions often fall short due to designers' limited understanding of elevator traffic dynamics. This research seeks to address these challenges by employing Gaussian analysis to comprehensively examine elevator traffic patterns within a typical office building context. By analyzing both actual monitored data and predictions generated by LS-SVMs, the study aims to offer valuable insights into elevator traffic behavior. Additionally, the research endeavors to serve as a valuable resource for ETA (Elevator Traffic Analysis), providing designers with a deeper understanding of elevator traffic dynamics and guiding the development of more effective solutions to alleviate congestion and improve passenger experience within vertical transportation systems. Through this approach, the study contributes to advancements in elevator design and operation, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments.