Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.23919/ECC55457.2022.9838059 | 2022 EUROPEAN CONTROL CONFERENCE (ECC) |
DocType | ISSN | Citations |
Conference | Presented at European Control Conference 2022 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Zhang | 1 | 0 | 0.34 |
| Athanasios Tsiligkaridis | 2 | 0 | 0.34 |
| Hiroshi Taguchi | 3 | 0 | 0.34 |
| Arvind U. Raghunathan | 4 | 163 | 20.63 |
| Daniel Nikovski | 5 | 165 | 31.87 |