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
• ISSN: 2327-4697; 2334-329X
• DOI: 10.1109/TNSE.2021.3126830

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, they mainly designed and/or developed single forecasting models for traffic forecasting. Such models may show ideal performance in some scenarios but lack satisfactory robustness to dynamic scenario changes. To address this challenge, a graph neural network-driven traffic forecasting model for CIoVs is proposed in this work, which is denoted as Gra-TF. In this paper, we regard the dynamics of traffic data as a temporal evolution scenario. With the assistance of ensemble learning, three typical graph-level prediction methods are employed to construct an integrated and enhanced forecasting model. This design utilizes several methods to minimize uncertainty in CIoVs. Finally, we use a real-world dataset to build an experimental scenario for further assessment. Numerical results indicate that the proposed Gra-TF improves the prediction accuracy by 30% to 40% compared with several baseline methods.