Prediction Method of Railway Freight Volume Based on Genetic Algorithm Improved General Regression Neural Network

Abstract

Railway freight transportation is an important part of the national economy. The accurate forecast of railway freight volume is significant to the planning, construction, operation, and decision-making of railways. Railway freight volume forecasting methods are complex and nonlinear due to the imbalance of supply and demand in the railway freight market as well as the complicated and different influences of various factors on freight volume. The relation between some information is easily ignored when the traditional method of railway freight volume forecasting is used for prediction based on causality or time series. After analyzing the application status of the generalized regression neural network (GRNN) in the prediction method of railway freight volume, this paper improves the performance of this model using an improved neural network. In the improved method, genetic algorithm (GA) is adopted to search the optimal spread, which is the only factor of the GRNN, and then the optimal spread is used for forecasting in the GRNN. In the process of railway freight volume forecasting, through this method, the increments of data are taken in the calculation process and the goal values are obtained after calculation as the forecasted results. Compared to the results of the GRNN, higher prediction accuracy is obtained through the GA-improved GRNN. Finally, the railway freight volumes in the next 2 years are forecasted based on this method and this improved method can provide a new approach for predicting the railway freight volume.

Railway Freight Demand Forecasting Based on Multiple Factors: Grey Relational Analysis and Deep Autoencoder Neural Networks

“…(4) FC-LSTM (fully connected LSTM): It is a classic RNN that learns time series and predicts through fully connected neural networks. In this paper, the hidden layer is set to be two layers; the hidden units are 32 and 64, respectively, the learning rate is 0.001, and the batch size is 64 [42]; (5) DNN (deep neural network): It uses DNN to extract railway freight demand characteristics and predict railway freight demand [43]; (6) FNN (feedforward neural networks): FNN is the most basic type of neural network, consisting of an input layer, hidden layer, and output layer, suitable for most classification and regression problems [44]; (7) GRNN (general recurrent neural networks): GRNN calculates the correlation density function between variables and carries out regression, making it suitable for time series prediction [45].…”

Combined Prediction Model of Gas Concentration Based on Indicators Dynamic Optimization and Bi-LSTMs

“…Bi-directional Long Short-Term Memory (Bi-LSTM) network [ 35 , 36 ] combines the information of the input sequence in both forward and backward directions based on the LSTM, i.e., the sequence is input into LSTM in forward and reverse order, respectively. Bi-LSTM makes the feature acquired at moment t to have information between the past and the future, which can effectively ensure the accuracy of time sequence prediction [ 37 , 38 ]. For the output at moment t , the forward LSTM layer has the information at moment t in the input sequence and the previous moments, while the backward LSTM layer has the information at moment t in the input sequence and the moments after.…”