Graph Neural Network-Driven Traffic Forecasting for the Connected Internet of Vehicles

• Published in: IEEE transactions on network science and engineering Vol. 9; no. 5; pp. 3015 - 3027
• Main Authors: Zhang, Qin, Yu, Keping, Guo, Zhiwei, Garg, Sahil, Rodrigues, Joel J. P. C., Hassan, Mohammad Mehedi, Guizani, Mohsen
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Convolution; Correlation; Data models; deep learning; Feature extraction; Forecasting; Graph neural networks; Internet of Vehicles; Neural networks; Roads; Robustness (mathematics); Stacking; traffic forecasting; Traffic information; Traffic models; Training; Vehicles; Wireless communications

Due to great advances in wireless communication, the connected Internet of vehicles (CIoVs) has become prevalent. Naturally, internal connections among active vehicles are an indispensable factor in traffic forecasting. Although many related research studies have been conducted in the past few years...