Open AccessResearch on Railway Passenger Volume Prediction Based on LSTM Neural Network
Author(s) -
Yan Xu,
Wei Xu,
Siwei Chen
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/688/4/044017
Subject(s) - artificial neural network , volume (thermodynamics) , computer science , mean squared prediction error , predictive modelling , artificial intelligence , machine learning , physics , quantum mechanics
Accurate railway passenger volume prediction plays an important role in the development of railway passenger transport industry. Taking the national railway passenger volume as an example, based on the data of 2005-2017, LSTM neural network prediction model is established. The prediction model results are compared with the actual situation, and the relative error rate is analyzed. The results show that the prediction model of LSTM neural network has low error and good prediction effect.