Transformer Networks for Predictive Group Elevator Control
| Autor: | Jing Zhang, Athanasios Tsiligkaridis, Hiroshi Taguchi, Arvind Raghunathan, Daniel Nikovski |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Physics - Physics and Society Computer Science - Machine Learning FOS: Electrical engineering electronic engineering information engineering FOS: Physical sciences Physics and Society (physics.soc-ph) Systems and Control (eess.SY) Electrical Engineering and Systems Science - Systems and Control Machine Learning (cs.LG) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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