Abdellah Ali R, Abdelmoaty Ahmed, Ateya Abdelhamied A, Abd El-Latif Ahmed A, Muthanna Ammar, Koucheryavy Andrey
Electrical Engineering Department, Faculty of Engineering, Al-Azhar University, Qena, Egypt.
EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia.
Front Artif Intell. 2025 Mar 18;8:1565287. doi: 10.3389/frai.2025.1565287. eCollection 2025.
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.
车与万物（V2X）通信有望彻底改变道路安全和效率。然而，数据共享和网络可靠性方面的挑战阻碍了其全面实现。本文通过提出一种用于V2X环境中交通预测的新型深度学习（DL）方法来应对这些挑战。我们采用双向长短期记忆（BiLSTM）网络，并将其性能与其他著名的DL架构进行比较，包括单向LSTM和门控循环单元（GRU）。我们的研究结果表明，BiLSTM模型在预测交通模式方面表现出更高的准确性。这种增强的预测能力能够实现更高效的资源分配、改善网络性能，并提高所有道路使用者的安全性，减少燃料消耗、降低排放，以及实现更可持续的交通系统。
