计算机科学
稳健性(进化)
人工神经网络
互联网
图形
人工智能
交通生成模型
数据建模
机器学习
数据挖掘
实时计算
化学
万维网
基因
理论计算机科学
数据库
生物化学
作者
Qin Zhang,Keping Yu,Zhiwei Guo,Sahil Garg,Joel J. P. C. Rodrigues,Mohammad Mehedi Hassan,Mohsen Guizani
出处
期刊:IEEE Transactions on Network Science and Engineering
[Institute of Electrical and Electronics Engineers]
日期:2022-09-01
卷期号:9 (5): 3015-3027
被引量:104
标识
DOI:10.1109/tnse.2021.3126830
摘要
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.
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