Railway container transportation time prediction based on CNN-GRU-Attention model
WANG Hui
SONG Rui
HE Wei
CAI Jinjin
LONG Zeyu
CONG Ming
Abstract：To address the challenge of accurately predicting container transportation time,this study proposes a hybrid model,CNN-GRU-Attention(CGA),which integrates Convolutional Neural Net-works(CNN),Gated Recurrent Units(GRU),and an Attention Mechanism(Attention).Key factors influencing railway container transportation time,such as transport distance and whether the shipment crosses bureau boundaries,are selected as input features.A sliding window approach is employed to segment the data before feeding it into the model.The CNN-GRU framework is used as the main framework to extract data features and capture long-term dependencies,while the attention module en-hances the model's ability to focus on critical information.The model's performance is evaluated using Mean Square Error(MSE),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Coefficient of Determination(R2),and Mean Absolute Percentage Error(MAPE).Representative machine learning and deep learning models are used as benchmarks for comparison.Results indicate that the CGA model achieves an MSE of 77.84,RMSE of 8.82,MAE of 2.72,R2 of 0.958,and MAPE of 4.47%.Compared with other models,the CGA model has demonstrates superior prediction accuracy for railway container transportation time and delivers better overall forecasting performance.
Keywords：railway transportrailway containerstransportation time predictionconvolutional neural networkattention mechanism
Publication Date：2025-08-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 115-121 )