Transformer Networks for Predictive Group Elevator Control
This work addresses elevator scheduling efficiency for building management, but it is incremental as it builds on existing predictive methods with specific optimizations.
The paper tackled predictive group elevator control by using a Transformer-based destination predictor and a linear regression model to forecast passenger arrivals, achieving up to 50% savings in Average Waiting Time for light arrival streams and around 15% for medium streams in down-peak traffic.
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.