Connected Vehicles Traffic Prediction
Emerging GNN Methods
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Beschreibung
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.
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
Autor*in
Quan Shi
Themen in »Connected Vehicles Traffic Prediction«
Connected vehicles Vehicular communications Traffic prediction Graph convolutional network Intelligent transportation system Spatial correlation