Name
Abstract | ||
|---|---|---|
Different from the existing train delay studies that had strived to explore sophisticated algorithms, this paper focuses on finding the bound of improvements on predicting multi-scenario train delays with different machine learning methods. Motivated by the observation of deep learning methods failing to improve the prediction performance if the delay occurs rarely, we present a novel augmented ma... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TITS.2021.3099031 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | DocType | Volume |
Delays,Predictive models,Resilience,Real-time systems,Time series analysis,Prediction algorithms,Random forests | Journal | 23 |
Issue | ISSN | Citations |
3 | 1524-9050 | 0 |
PageRank | References | Authors |
0.34 | 0 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianqing Wu | 1 | 0 | 0.34 |
| Yihui Wang | 2 | 4 | 1.45 |
| Bo Du | 3 | 1662 | 130.01 |
| Qiang Wu | 4 | 304 | 40.42 |
| Yanlong Zhai | 5 | 26 | 5.11 |
| Jun Chen | 6 | 1 | 3.41 |
| Luping Zhou | 7 | 498 | 43.89 |
| Chen Cai | 8 | 0 | 0.34 |
| Wei Wei | 9 | 507 | 68.07 |
| Qingguo Zhou | 10 | 103 | 29.48 |