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Title	A Comparative Study Of Artificial Intelligence Algorithms For Network Traffic Prediction In Vanet
ID_Doc	784
Authors	Sepasgozar S.S.; Pierre S.
Year	2022
Published	International Conference on Wireless and Mobile Computing, Networking and Communications, 2022-October
DOI	http://dx.doi.org/10.1109/WiMob55322.2022.9941664
Abstract	Increasing the number of vehicles and their communications in smart cities is a critical issue that leads to road and network traffic. Traffic prediction with high accuracy and less complexity is a challenge in Intelligent Transportation System (ITS). In Artificial Intelligence (AI), Machine learning (ML) algorithms are promising solutions to prediction problems, and Deep Learning (DL) algorithms are used for more complicated issues. In this paper, we propose a comparative analysis of the prediction performance of the most five common AI algorithms used to solve classification problems. Different evaluation metrics are employed to analyze algorithms and get the most accurate one for selecting such problems. Simulation results on the Vehicular Ad-Hoc Network (VANET) dataset revealed that Random Forest (RF) as a traditional ML algorithm performed better than other algorithms in terms of accuracy (96%) and execution time (0.73 minute) for traffic prediction. © 2022 IEEE.
Author Keywords	Artificial Intelligence; Intelligent Transportation System; machine learning method; network traffic prediction; neural network; Vehicular Ad-Hoc Network

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