Prediction of Return Trips of China-Europe Railway Express Based on GRA-ARIMA-LSTM Combination Model
With the advancement of the"Belt and Road"initiative,the China-Europe Railway Express has not only strengthened the economic ties between China and the countries along the route but also pro-moted cultural and technological exchanges.By the end of 2023,the China-Europe Railway Express ser-vice has covered 217 cities in 25 European countries,having operated 17,523 runs and shipped 1.9 million TEUs.However,there is significant imbalance in the operation of outbound and return trains,which ad-versely affects the reduction of operating costs and the improvement of efficiency for the China-Europe Railway Express.Therefore,accurately predicting the number of return trains is crucial for assessing the fu-ture development of the railway transportation market and further adjusting and optimizing the balance of outbound and return trains.This paper addresses the prediction of the number of return trains for the China-Europe Railway Express by proposing a prediction method based on a Grey Relational Analysis-Autoregressive Integrated Moving Average-Long Short-Term Memory Network(GRA-ARIMA-LSTM)combination model.First,the GRA method is used to select highly correlated influencing factors as inputs for the neural network,and the ARIMA model processes the historical information of the time series data of the return train number to ob-tain linear predictions and their residual sequences.Subsequently,the LSTM model is employed to conduct in-depth study of these residuals and other related factors to predict the nonlinear factors in the residual se-quence.Finally,using three metrics:Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and the Coefficient of Determination(R2),the combination model's prediction results are com-pared with those of two single models.The research results indicate that the GRA-ARIMA-LSTM combi-nation model has metrics of R2=0.9876,MAPE=0.0124,and RMSE=0.083,showing that the combination model achieves the highest prediction accuracy and the smallest error,making it more suitable for the predict-ing the number of return trains for the China-Europe Railway Express.The research of this paper not only provides theoretical support for the rational scheduling of capacity resources for the China-Europe Railway Express but also holds significant importance for improving prediction accuracy and decision-making effi-ciency.
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