An AI-driven prediction system that analyses building occupancy patterns to anticipate elevator demand and pre-position cars before peak flows occur.
Full Definition
Lift traffic forecasting applies machine learning algorithms to historical call data, building occupancy schedules, calendar events, and real-time access control feeds to predict future elevator demand. The system pre-positions cars at floors where demand is anticipated, reducing average waiting times by 15–30% compared to reactive dispatch. Forecasting models are retrained continuously as building usage patterns evolve. Integration with destination dispatch and group control systems enables seamless implementation. Forecasting outputs are also used for maintenance scheduling, energy management, and capacity planning during building expansion or change of use.
