Research On Intelligent Forecast Model And Algorithm For Railway Freight Volume

Railway freight volume forecasting can provide a theoretical basis for the rational planning and layout of future railway construction,and it can play a decisive role in effectively increasing the market share of railway transportation,enhancing market competitiveness and developing logistics industry economy.As a complex dynamic logistics industry chain system,railway transportation covers many aspects and involves many influencing factors,and it also has the characteristics of nonlinearity and volatility.Most of the traditional forecasting models are based on a single index and structure,and the forecasting models based on this have some limitations,which are difficult to describe accurately and completely the current distribution law of railway freight volume.In this context,it is necessary to develop a scientific,efficient and highprecision railway freight volume prediction model.In this paper,based on the current research status of railway freight volume forecasting and the deficiencies of the main models,this paper use the feature reduction algorithm of rough set theory to explore the feature relationship between railway freight volume and key impact indicators,and establish a scientific predictive input indicator system.Secondly,LSTM neural network,which has strong memory characteristics and can effectively learn long-term dependence ability,is selected as the basic model of railway freight volume prediction.Based on the traditional LSTM model,a railway freight volume prediction model based on the BILSTM model with peephole connection is proposed.Finally,on the basis of the sensitivity of the prediction results to the parameters,the particle swarm optimization algorithm is adopted to realize the global optimization of the neural network model parameters and,on this basis,adjusts the inertia factor and adopts the genetic algorithm to improve the optimization ability of the particle swarm optimization algorithm Improve,and it can effectively avoid falling into the local minimum.Through simulation experiments and model comparison and analysis,the BILSTM model with peephole connection has a good ability to learn long-term dependencies and complete training.The improved particle swarm optimization algorithm can effectively improve the algorithm’s optimization ability,optimization speed and stability.The improved particle swarm optimization neural network combined model has greater ability and accuracy in the prediction of railway freight volume and can be used as an effective railway freight volume prediction model.