  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.
