Abstract
Vehicular ad hoc networks (VANETs) are a promising technology that enables the communication between vehicles on roads. It becomes an emerging topic that integrates the capabilities of new generation wireless networks for vehicles. Network traffic prediction allows Intelligent Transport Systems (ITS) for proactive response to events before they happen. With the rapid increase in the amount, quality, and detail of traffic data, there is a need for new techniques that can use the information in the data to provide better results while they can scale and cope with increasing amounts of data and growing cities. In this paper, we purpose (Long Short-Term Memory) LSTM deep learning for the prediction of VANET network traffic. We have trained the models using traffic data collected from the VANET network. The prediction accuracy has been evaluated using RMSE as a merit function and another measure of prediction accuracy is the mean absolute percentage error (MAPE).
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Abdellah, A.R., Koucheryavy, A. (2020). VANET Traffic Prediction Using LSTM with Deep Neural Network Learning. In: Galinina, O., Andreev, S., Balandin, S., Koucheryavy, Y. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN ruSMART 2020 2020. Lecture Notes in Computer Science(), vol 12525. Springer, Cham. https://doi.org/10.1007/978-3-030-65726-0_25
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