Short-Term Traffic Flow Prediction Based on Wavelet Neural Network

In this research, we programmed a wavelet neural network (WNN) implementation in the MATLAB environment to achieve short-term traffic flow prediction. The wavelet neural network represents an innovative neural network architecture that combines the advantages of wavelet transform and neural networks, enabling efficient processing of nonlinear and non-stationary signals. Our implementation involves several key components: the wavelet basis function selection using Morlet or Mexican hat wavelets, backpropagation algorithm for network training, and traffic data preprocessing modules. The MATLAB code structure includes data normalization functions, wavelet coefficient calculation routines, and network training scripts with adjustable hidden layers and learning rates. Through experimental validation and data analysis, we established significant conclusions regarding traffic flow forecasting and demonstrated that the wavelet neural network achieves high accuracy and reliability in this domain. The algorithm particularly excels in capturing temporal patterns and sudden changes in traffic flow data. Therefore, we conclude that the wavelet neural network presents a highly promising methodology for short-term traffic flow prediction, with implementation advantages in handling real-time data fluctuations and complex traffic patterns.