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Deep learning neural networks-based traffic predictors for V2X communication networks.

Marina Magdy Saady1, Hatim Ghazi Zaini2, Mohamed Hassan Essai3

  • 1Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, Egypt.

Frontiers in Artificial Intelligence
|January 2, 2026
PubMed
Summary

AI models like GRU and CNNs enhance vehicle-to-everything (V2X) traffic prediction for safer roads. These advanced Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) improve traffic management and efficiency.

Keywords:
CNNRNNV2Xdeep learningoptimizerstraffic prediction

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Area of Science:

  • Artificial Intelligence
  • Telecommunications Engineering
  • Network Security

Background:

  • Vehicle-to-everything (V2X) communication is crucial for 5G and beyond networks, aiming to boost road safety and traffic efficiency.
  • Effective traffic information sharing remains a challenge, hindering the full potential of V2X systems.
  • AI-based traffic prediction offers a solution to optimize traffic management and network performance.

Purpose of the Study:

  • To investigate the application of Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) for accurate and efficient V2X traffic prediction.
  • To evaluate the impact of hyperparameters, including loss functions and optimizers, on model performance.
  • To compare the effectiveness of different AI models for V2X traffic prediction.

Main Methods:

  • Utilized Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (BiLSTM) models for traffic prediction.
  • Employed Convolutional Neural Networks (CNNs) with various activation functions for traffic pattern analysis.
  • Optimized model performance using different loss functions (e.g., Mean Squared Error - MSE) and optimizers (e.g., Adam).

Main Results:

  • GRU models with MSE loss and Adam optimizer demonstrated superior accuracy and computational efficiency over LSTM and BiLSTM models.
  • CNN models with Rectified Linear Unit (ReLU) activation and Adam optimizer showed excellent performance in terms of Root Mean Square Error (RMSE) and computational complexity.
  • The proposed AI models outperformed existing methods in accuracy, efficiency, and robustness for V2X traffic prediction.

Conclusions:

  • AI-based traffic prediction using GRU and CNN models significantly enhances V2X communication capabilities.
  • Optimized hyperparameter tuning is critical for achieving high accuracy and efficiency in V2X traffic prediction.
  • The developed models offer a robust solution for improving traffic management, congestion mitigation, and network reliability.