Name
Title | ||
|---|---|---|
Short-Term Travel Speed Prediction for Urban Expressways: Hybrid Convolutional Neural Network Models |
Abstract | ||
|---|---|---|
Deep learning models for short-term travel speed prediction on urban expressways, such as the convolutional neural network (CNN), still present several limitations in multiscale spatiotemporal feature extraction. Hence, in this paper, three hybrid CNN models are proposed to improve the basic CNN model with regard to three target aspects for short-term (i.e., 5 min) travel speed prediction on urban... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TITS.2020.3027628 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | DocType | Volume |
Predictive models,Data models,Feature extraction,Neural networks,Spatiotemporal phenomena,Detectors,Deep learning | Journal | 23 |
Issue | ISSN | Citations |
3 | 1524-9050 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Keshuang Tang | 1 | 9 | 3.67 |
| Siqu Chen | 2 | 0 | 0.34 |
| Yumin Cao | 3 | 0 | 0.34 |
| Xiaosong Li | 4 | 0 | 0.34 |
| Di Zang | 5 | 98 | 12.40 |
| Jian Sun | 6 | 60 | 14.76 |
| Yangbeibei Ji | 7 | 0 | 0.34 |