Connected Vehicles Traffic Prediction

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.

• Erscheinungsdatum: 29.04.2025
• Autor/Autorin: Quan Shi,Yinxin Bao,Qinqin Shen,Zhenquan Shi,Ruifeng Gao
• Reihe: Engineering (R0)
• Format: E-Book
• Dateiformat: PDF
• Kopierschutz: Wasserzeichen
• Dateigröße: 26.9 MB
• Verlag: SPRINGER
• Sprache: Englisch
• Umfang: 180 Seiten
• ISBN: 9783031845482
• Lieferung: Sofort per Download
• Hinweis: Sofort per Download lieferbar. Kein physischer Versand.
• Kompatibilität: Lesbar auf Geräten und Apps mit PDF-Unterstützung.