An Automatic Prediction of Elevator Capacity Using ML to Customize the Total Capacity

Elevators serve as vital components in modern buildings, yet optimizing passengers' waiting time remains a crucial challenge. This study proposes a machine learning-based approach to enhance the efficiency of elevator systems by minimizing the waiting time experienced by passengers. By leveraging historical data and predictive models, this research aims to optimize elevator operations, considering factors such as traffic patterns, peak hours, and user demand. The implementation of machine learning algorithms enables the system to dynamically adjust elevator scheduling strategies, ultimately reducing waiting times and enhancing user experience. The findings highlight the potential of machine learning techniques in revolutionizing elevator management for improved passenger satisfaction and operational efficiency.