Transformer Networks for Predictive Group Elevator Control
- Resource Type
- Conference
- Authors
- Zhang, Jing; Tsiligkaridis, Athanasios; Taguchi, Hiroshi; Raghunathan, Arvind; Nikovski, Daniel
- Source
- 2022 European Control Conference (ECC) European Control Conference (ECC), 2022. :1429-1435 Jul, 2022
- Subject
- Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Transportation
Training
Schedules
Privacy
Smart buildings
Linear regression
Transfer learning
Predictive models
- Language
We propose a Predictive Group Elevator Scheduler by using predictive information of passengers arrivals from a Transformer based destination predictor and a linear regression model that predicts remaining time to destinations. Through extensive empirical evaluation, we find that the savings of Average Waiting Time (AWT) could be as high as above 50% for light arrival streams and around 15% for medium arrival streams in afternoon down-peak traffic regimes. Such results can be obtained after carefully setting the Predicted Probability of Going to Elevator (PPGE) threshold, thus avoiding a majority of false predictions for people heading to the elevator, while achieving as high as 80% of true predictive elevator landings as early as after having seen only 60% of the whole trajectory of a passenger.