Short-Term Traffic Flow Prediction Using Wavelet Neural Networks

Wavelet neural networks represent a hybrid model combining the strengths of wavelet analysis and neural networks, particularly suitable for predicting non-stationary signals like short-term traffic flow. Traditional neural networks may underperform when handling traffic data characterized by sudden changes and non-stationary properties. Wavelet transform effectively captures local signal features, and when integrated with neural networks' learning capabilities, significantly enhances prediction accuracy.

Implementing wavelet neural networks for short-term traffic flow prediction in MATLAB typically involves these key steps:

Data Preprocessing Traffic flow data often contains noise and missing values requiring initial cleaning and normalization. Wavelet denoising techniques can smooth data by removing random noise, while Min-Max normalization helps accelerate neural network training. In MATLAB, this can be implemented using wden for wavelet denoising and mapminmax for normalization.

Wavelet Decomposition Original traffic flow signals undergo wavelet decomposition into approximation and detail components at different scales. Commonly used mother wavelets include db (Daubechies) and sym (Symlets) series. This decomposition extracts features across frequency domains for subsequent neural network processing. MATLAB's wavedec function performs multi-level 1D wavelet decomposition with specified wavelet type and decomposition level.

Neural Network Modeling Feedforward neural networks (BPNN) or radial basis function neural networks (RBFNN) typically serve as prediction models. The input layer receives wavelet-decomposed components, hidden layers perform feature learning, and the output layer predicts future traffic flow. MATLAB's Neural Network Toolbox provides feedforwardnet for BPNN implementation and newrb for RBF network creation.

Training and Optimization Historical data trains the network, with parameters like learning rate, hidden layer nodes, and training iterations adjusted through cross-validation. Genetic algorithms (GA) or particle swarm optimization (PSO) can optimize neural network architecture using MATLAB's Global Optimization Toolbox functions like ga and particleswarm to further improve prediction accuracy.

Prediction and Evaluation The trained model predicts short-term traffic flow, with common evaluation metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and correlation coefficient (R²). MATLAB's predict function generates forecasts, while error metrics can be calculated using rmse, mae, and corrcoef functions.

This approach's advantage lies in simultaneously leveraging wavelet transform's multi-resolution analysis capability and neural networks' nonlinear fitting ability, outperforming traditional time series models (like ARIMA) under sudden traffic condition changes.