Railway Freight Prediction Based on Stage Segmentation and Big Data Method

In the operation of railway transportation enterprises, having prior knowledge of future loading volumes and trends at freight stations is crucial for optimal deployment of empty cars and the development of daily operational plans. Long-term freight volumes at railway stations often exhibit cyclical patterns influenced by seasonal fluctuations, holidays and other factors. Additionally, changes in freight volume are significantly affected by the proportion of freight from various industries. To address these dynamics, this study proposes a hybrid prediction model for long-term loading volumes at railway freight stations. This model predicts by stage segmentation through peak-valley segmentation (PVS), variational mode decomposition (VMD) and an attention mechanism integrated into a temporal convolutional network (TCN). Using historical freight volumes from the Shuohuang railway freight station as a case study, we employed mean absolute percentage error (MAPE), mean absolute error (MAE) and root mean square error (RMSE) as evaluation metrics to assess the combined predictive performance of the PVS, VMD and TCN methods. The experimental results show that the PVS-VMD-A-TCN model significantly improves the accuracy of long-term freight volume predictions. Compared to traditional methods such as ARIMA, GRU and TCN, it exhibits superior predictive performance, offering a new approach for accurately forecasting long-term loading volumes.