Connected Vehicles Traffic Prediction Emerging GNN Methods /
This book delves into the problems and challenges faced in achieving improved performance in connected vehicles traffic flow prediction in intelligent connected transportation systems and provides an in-depth analysis of spatial-temporal feature extraction, global local spatial feature extraction, a...
Sábháilte in:
| Príomhchruthaitheoirí: | , , , , |
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| Údar corparáideach: | |
| Formáid: | Leictreonach Ríomhleabhar |
| Teanga: | Béarla |
| Foilsithe / Cruthaithe: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
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| Eagrán: | 1st ed. 2025. |
| Sraith: | Wireless Networks,
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| Ábhair: | |
| Rochtain ar líne: | https://doi.org/10.1007/978-3-031-84548-2 |
| Clibeanna: |
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Clár na nÁbhar:
- Introduction
- Artificial Intelligence in Connected Vehicles
- A Hybrid Model Integrating Local and Global Spatial Correlation for Connected Vehicles Traffic Prediction
- Sdscnn: A Hybrid Model Integrating Static and Dynamic Spatial Correlation Neural Network For Connected Vehicles Traffic Prediction
- Spatial-Temporal Complex Graph Convolution Network for Connected Vehicles Traffic Prediction
- Prior Knowledge Enhanced Time-Varying Graph Convolution Network for Connected Vehicles Traffic Prediction
- Spatial-Temporal Heterogeneous and Synchronous Graph Convolution Network For Connected Vehicles Traffic Prediction
- Multi-Sequential Temporal Convolution Gated Graph Neural Network For Connected Vehicles Traffic Prediction
- Connected Vehicles Traffic Prediction Based On Multi-Temporal Graph Convolutional Networks
- Urban Road Network Connected Vehicles Traffic Speed Prediction Model Based On Global Spatio-Temporal Characteristics
- Future Challenges Of Connected Vehicles Traffic Prediction
- Conclusion.