Connected Vehicles Traffic Prediction

Emerging GNN Methods

Inbunden, Engelska, 2025

Av Quan Shi, Yinxin Bao, Qinqin Shen, Zhenquan Shi, Ruifeng Gao

2 339 kr

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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.

Produktinformation

Utgivningsdatum2025-04-30
Mått155 x 235 x 17 mm
Vikt457 g
FormatInbunden
SpråkEngelska
SerieWireless Networks
Antal sidor180
FörlagSpringer International Publishing AG
ISBN9783031845475

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Prof. Quan Shi received the M.S. and Ph.D. degrees in Computer Science Technology and Management Information Systems from the University of Shanghai for Science and Technology, Shanghai, China, in 2005 and 2011, respectively. He is currently a Professor with the School of Transportation and Civil Engineering, Nantong University. His research interests include the Intelligent Information Processing, Deep Learning, Data Mining, Traffic Information and Control,,and Big Data Techniques for Computer. Dr. Yinxin Bao is a Ph.D. student majoring in Information and Communication Engineering in 2021 at the School of Information Science and Technology, Nantong University, with research interests in Intelligent Transportation, Deep Learning, Data Mining, and computer vision. He is currently serving as a reviewer for SCI journals Engineering Applications of Artificial Intelligence and Alexandria Engineering Journal. Assoc. Prof. Qinqin Shen received the Ph.D. degree from the School of Rail Transportation, Soochow University, in 2021. She is currently an assistant professor at the School of Transportation and Civil Engineering, Nantong University. She has published over ten articles in high-level journals, including Computational and Applied Mathematics, Computers and Mathematics with Applications, and Numerical Algorithms. Her research interests include Intelligence Transportation and Numerical Computation. Prof. Zhenquan Shi received the master’s degree from the School of Computer Science and Technology, University of Shanghai for Science and Technology, in 2009, and the Ph.D. degree in Management Information Systems from the School of Management, University of Shanghai for Science and Technology, in 2021. He is currently working with the School of Transportation and Civil Engineering, Nantong University. He has published eight relevant articles in high-level journals. His main research interests include Intelligent Transportation and Deep Learning. Assoc. Prof. Ruifeng Gao received the B.S. degree from Central South University, Changsha, China, in 2009, and the M.S. and Ph.D. degrees from Nantong University, Nantong, China, in 2013 and 2019, respectively. From 2019 to 2020, he was a Visiting Scholar with the Singapore University of Technology and Design. He is currently an Associate Professor with the School of Transportation and Civil Engineering, Nantong University. His main research interests include Maritime Communication Networks, Resource Management, and Machine Learning.

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.