Freight Volume Prediction Using GRNN

Freight volume prediction based on Generalized Regression Neural Network (GRNN) is a method that utilizes neural network models to forecast cargo transportation volumes. GRNN is a powerful machine learning model that can predict future freight volumes by learning from historical freight data and other relevant factors. This approach helps enterprises and logistics companies better plan and manage their transportation activities, improving operational efficiency and reducing costs. Implementation typically involves preprocessing historical data (e.g., normalization), configuring GRNN parameters such as the spread factor, and training the network using MATLAB's newgrnn function. The algorithm employs radial basis functions and a normalized architecture that requires no iterative training, making it particularly suitable for regression problems with limited data samples. Additionally, this method can be applied to market research and forecasting, enabling businesses to make more accurate decisions and prepare in advance. Therefore, GRNN-based freight volume prediction serves as a valuable and essential tool that plays a significant role in logistics and supply chain management domains.