Abstract: The accuracy of railway freight volume forecasting is necessary for railway transportation companies to make marketing plans and marketing decisions, especially the impact of short-term railway freight volume is crucial. In order to improve the prediction accuracy of railway freight volume, this paper establishes a predictive model by optimizing the parameters of the long-short term memory network (GS-LSTM) and by using the grid search algorithm to optimize the most important parameters (batch size, number of hidden layer neural units and learning rate) in the LSTM model training network. Based on the monthly railway freight volume data from January 2005 to July 2021, firstly, BP and LSTM models are established to compare the prediction results. The MAPE of the LSTM model is 1.55 percentage points lower than that of the BP model. Then the network parameters of the BP and LSTM models are optimized and compared, the two optimized models have improved prediction effects than the basic model and the optimized LSTM model is further reduced by 0.18 percentage points than the BP model. The experimental results show that the optimized LSTM model has better prediction effect and better generalization ability, and has good research and utilization value. 

Key words: long short-term memory model, grid search algorithm, BP neural network, railway freight volume, time series prediction