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Connected vehicles traffic predictio...

Shi, Quan.

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Connected vehicles traffic prediction = emerging GNN methods /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Connected vehicles traffic prediction/ by Quan Shi ... [et al.].
其他題名:	emerging GNN methods /
其他作者:	Shi, Quan.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	ix, 180 p. :ill. (chiefly color), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Intelligent transportation systems. -
電子資源:	https://doi.org/10.1007/978-3-031-84548-2
ISBN:	9783031845482

Connected vehicles traffic prediction = emerging GNN methods /
Connected vehicles traffic predictionemerging GNN methods /[electronic resource] :by Quan Shi ... [et al.]. - Cham :Springer Nature Switzerland :2025. - ix, 180 p. :ill. (chiefly color), digital ;24 cm. - Wireless networks,2366-1445. - Wireless networks..

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.

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, and fusion of external factors. The book is divided into ten chapters, and the introductory section presents the history of the development of artificial intelligence and graph neural networks in the context of connected vehicles, related work on prediction of connected traffic, and preliminary knowledge. Chapter 2 to 9 present eight prediction methods in the context of connected traffic, respectively. Each section includes an introduction to the problem definition, model architecture, experimental setup, and discussion of results, as well as references. The last section summarizes the contributions of the book and future challenges. Covers performance in connected vehicles traffic flow prediction in intelligent connected transportation systems; Presents connected traffic flow prediction solutions that ensure model performance; Proposes solutions demonstrated with proof-of-concept prototype implementations, written in open-source Python.

ISBN: 9783031845482

Standard No.: 10.1007/978-3-031-84548-2doiSubjects--Topical Terms:

1066364
Intelligent transportation systems.

LC Class. No.: TE228.3

Dewey Class. No.: 388.312

Connected vehicles traffic prediction = emerging GNN methods /
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505 0 $a 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.
520 $a 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, and fusion of external factors. The book is divided into ten chapters, and the introductory section presents the history of the development of artificial intelligence and graph neural networks in the context of connected vehicles, related work on prediction of connected traffic, and preliminary knowledge. Chapter 2 to 9 present eight prediction methods in the context of connected traffic, respectively. Each section includes an introduction to the problem definition, model architecture, experimental setup, and discussion of results, as well as references. The last section summarizes the contributions of the book and future challenges. Covers performance in connected vehicles traffic flow prediction in intelligent connected transportation systems; Presents connected traffic flow prediction solutions that ensure model performance; Proposes solutions demonstrated with proof-of-concept prototype implementations, written in open-source Python.
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