Name
Title | ||
|---|---|---|
Modeling train timetables as images: A cost-sensitive deep learning framework for delay propagation pattern recognition |
Abstract | ||
|---|---|---|
•A hybrid deep learning model was developed for delay propagation patterns.•Train timetables were modeled as images.•A cost-sensitive technique was used to address the data imbalance challenge.•The proposed model shows satisfactory performance on different situations. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.eswa.2021.114996 | Expert Systems with Applications |
Keywords | DocType | Volume |
Train delay propagation,Pattern recognition,Train timetables,Images,Imbalanced data | Journal | 177 |
ISSN | Citations | PageRank |
0957-4174 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ping Huang | 1 | 1 | 0.70 |
| Zhongcan Li | 2 | 1 | 0.70 |
| Chao Wen | 3 | 17 | 6.00 |
| Javad Lessan | 4 | 4 | 1.49 |
| Francesco Corman | 5 | 0 | 0.34 |
| Liping Fu | 6 | 1 | 0.70 |