LEADER 04579nam 22006375 450 001 9910999676003321 005 20250429130219.0 010 $a3-031-84548-X 024 7 $a10.1007/978-3-031-84548-2 035 $a(CKB)38641758700041 035 $a(DE-He213)978-3-031-84548-2 035 $a(MiAaPQ)EBC32060688 035 $a(Au-PeEL)EBL32060688 035 $a(EXLCZ)9938641758700041 100 $a20250429d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aConnected Vehicles Traffic Prediction $eEmerging GNN Methods /$fby Quan Shi, Yinxin Bao, Qinqin Shen, Zhenquan Shi, Ruifeng Gao 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (IX, 180 p. 108 illus., 90 illus. in color.) 225 1 $aWireless Networks,$x2366-1445 311 08$a3-031-84547-1 327 $aIntroduction -- 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. 330 $aThis book delves into the problems and challenges faced in achieving improved performance in connected vehicles traffic flow prediction in intelligent connected transportation systems and provides an in-depth analysis of spatial-temporal feature extraction, global local spatial feature extraction, and fusion of external factors. The book is divided into ten chapters, and the introductory section presents the history of the development of artificial intelligence and graph neural networks in the context of connected vehicles, related work on prediction of connected traffic, and preliminary knowledge. Chapter 2 to 9 present eight prediction methods in the context of connected traffic, respectively. Each section includes an introduction to the problem definition, model architecture, experimental setup, and discussion of results, as well as references. The last section summarizes the contributions of the book and future challenges. Covers performance in connected vehicles traffic flow prediction in intelligent connected transportation systems; Presents connected traffic flow prediction solutions that ensure model performance; Proposes solutions demonstrated with proof-of-concept prototype implementations, written in open-source Python. 410 0$aWireless Networks,$x2366-1445 606 $aTelecommunication 606 $aComputational intelligence 606 $aTransportation engineering 606 $aTraffic engineering 606 $aCommunications Engineering, Networks 606 $aComputational Intelligence 606 $aTransportation Technology and Traffic Engineering 615 0$aTelecommunication. 615 0$aComputational intelligence. 615 0$aTransportation engineering. 615 0$aTraffic engineering. 615 14$aCommunications Engineering, Networks. 615 24$aComputational Intelligence. 615 24$aTransportation Technology and Traffic Engineering. 676 $a621.382 700 $aShi$b Quan$4aut$4http://id.loc.gov/vocabulary/relators/aut$01817218 702 $aBao$b Yinxin$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aShen$b Qinqin$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aShi$b Zhenquan$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aGao$b Ruifeng$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910999676003321 996 $aConnected Vehicles Traffic Prediction$94374767 997 $aUNINA