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Title	A Soft Voting Classification Model For Network Traffic Prediction In Vanet/V2X
ID_Doc	4845
Authors	Doval Amiri P.A.; Pierre S.
Year	2023
Published	International Conference on Wireless and Mobile Computing, Networking and Communications, 2023-June
DOI	http://dx.doi.org/10.1109/WiMob58348.2023.10187731
Abstract	Vehicular network services in the smart cities generate enormous data by vehicular road users, which is a critical challenge. Network traffic leads to a negative impact on safety applications. AI techniques are a promising solution to address network traffic in VANETs with V2X data. In this paper, we propose a soft voting classification model, which consists of hybrid supervised machine learning algorithms to predict traffic in the network. We evaluate the prediction performance of five well-known machine learning models and the proposed model based on various classification evaluation metrics. The simulation results show that the proposed network traffic prediction model performs better than other considered machine learning models in terms of accuracy (0.94%), time consumption (12.25 seconds) and AUROC (0.907) that proves its stability. © 2023 IEEE.
Author Keywords	artificial intelligence; ensemble learning; intelligent transportation system; machine learning; network traffic prediction; Vehicular ad-hoc networks

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