Accurate V2X traffic prediction with deep learning architectures

Ali R Abdellah; Ahmed Abdelmoaty; Abdelhamied A Ateya; Ahmed A Abd El-Latif; Ammar Muthanna; Andrey Koucheryavy

doi:10.3389/frai.2025.1565287

Accurate V2X traffic prediction with deep learning architectures

Front Artif Intell. 2025 Mar 18:8:1565287. doi: 10.3389/frai.2025.1565287. eCollection 2025.

Authors

Ali R Abdellah¹, Ahmed Abdelmoaty¹, Abdelhamied A Ateya^{2

3}, Ahmed A Abd El-Latif^{2

4}, Ammar Muthanna^{5

6}, Andrey Koucheryavy⁵

Affiliations

¹ Electrical Engineering Department, Faculty of Engineering, Al-Azhar University, Qena, Egypt.
² EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia.
³ Department of Electronics and Communications Engineering, Zagazig University, Zagazig, Egypt.
⁴ Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-Koom, Egypt.
⁵ The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, St. Petersburg, Russia.
⁶ Applied Probability and Informatics, 'Peoples' Friendship University of Russia (RUDN University), Moscow, Russia.

Abstract

Vehicle-to-Everything (V2X) communication promises to revolutionize road safety and efficiency. However, challenges in data sharing and network reliability impede its full realization. This paper addresses these challenges by proposing a novel Deep Learning (DL) approach for traffic prediction in V2X environments. We employ Bidirectional Long Short-Term Memory (BiLSTM) networks and compare their performance against other prominent DL architectures, including unidirectional LSTM and Gated Recurrent Unit (GRU). Our findings demonstrate that the BiLSTM model exhibits superior accuracy in predicting traffic patterns. This enhanced prediction capability enables more efficient resource allocation, improved network performance, and enhanced safety for all road users, reducing fuel consumption, decreased emissions, and a more sustainable transportation system.

Keywords: 5G and beyond; AI; BiLSTM; GRU; LSTM; V2X; deep learning.