Neural Network-Based Traffic Flow Prediction in MATLAB

This article shares insights on traffic flow prediction and optimization techniques using MATLAB's neural network capabilities. We demonstrate how neural networks serve as powerful tools for analyzing large datasets and forecasting future traffic patterns. The implementation utilizes MATLAB's Neural Network Toolbox, where we configure multi-layer perceptron (MLP) architectures with appropriate activation functions and training algorithms like Levenberg-Marquardt backpropagation. The provided source code includes data preprocessing steps such as normalization and time-series windowing, followed by network training with cross-validation techniques to prevent overfitting. Optimization focuses on hyperparameter tuning through grid search methods and performance evaluation metrics like RMSE (Root Mean Square Error). By refining prediction accuracy, we gain better insights into traffic conditions, enabling proactive traffic management strategies. The complete MATLAB source code is available for reference, incorporating comments on key functions including net.trainParam settings and predict() implementations. For any technical inquiries or suggestions regarding the code structure or optimization approaches, please feel free to contact me.