Prediction of Urban Rail Transit Sectional Passenger Flow Based on Elman Neural Network

This paper based on the feature of Beijing urban rail transit sectional passenger flow, combined with Elman neural network. After carrying out modeling experiment many times, a reasonable forecast model about the prediction of urban rail transit sectional passenger flow was established. Then the Elman neural network model was used to predict the sectional passenger flow of Beijing Subway Line 1, from Xidan station to Fuxingmen Station. At last the output results was compared with that of BP neural network, the result shows that the Elman neural network is more precise and effective than the BP neural network in the prediction of urban rail transit sectional passenger flow.