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

## TL;DR

This paper explores using deep learning models to predict V2X traffic, finding that GRU and CNN models with specific configurations perform best.

## Contribution

The study introduces optimized GRU and CNN models for V2X traffic prediction with improved accuracy and efficiency.

## Key findings

- GRU models with MSE loss and Adam optimizer outperform LSTM and BiLSTM in accuracy and efficiency.
- CNN models using ReLU activation and Adam optimizer show superior performance in RMSE and complexity.
- Proposed models demonstrate advantages over existing methods in accuracy, efficiency, and robustness.

## Abstract

Vehicle-to-everything (V2X) communication is a promising technology for enhancing road safety, traffic efficiency, and the availability of infotainment services in 5G networks and beyond networks. However, the effective sharing of traffic information remains a significant challenge. To address this, AI-based systems offer potential solutions. By predicting traffic patterns on dense networks, these systems can improve traffic management, mitigate congestion, increase network safety and reliability, and improve energy efficiency. This research investigates the application of Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) for accurate and efficient V2X traffic prediction. We explored the impact of various hyperparameters, including loss functions and optimizers, on the performance of these models. Our findings indicate that Gated Recurrent Unit (GRU) models, particularly with the Mean Squared Error (MSE) loss function and Adam optimizer, consistently outperform Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM) models in terms of both accuracy and computational efficiency. For CNN models, the Rectified Linear Unit (ReLU) activation function, coupled with the Adam optimizer, demonstrated superior performance in terms of Root Mean Square Error (RMSE) and computational complexity. By comparing our results with existing literature, we highlight the advantages of our proposed models in terms of accuracy, efficiency, and robustness.