Deep Learning for Predicting Traffic in V2X Networks

Abstract

Artificial intelligence (AI) is capable of addressing the complexities and difficulties of fifth-generation (5G) mobile networks and beyond. In this paradigm, it is important to predict network metrics to meet future network requirements. Vehicle-to-everything (V2X) networks are promising wireless communication methods where traffic information exchange in an intelligent transportation system (ITS) still faces challenges, such as V2X communication congestion when many vehicles suddenly appear in an area. In this paper, a deep learning algorithm (DL) based on the unidirectional long short-term memory (LSTM) model is proposed to predict traffic in V2X networks. The prediction problems are studied in different cases depending on the number of packets sent per second. The prediction accuracy is measured in terms of root-mean-square error (RMSE), mean absolute percentage error (MAPE), and processing time.