铁道科学与工程学报Issue(5)：1227-1231,5.
基于灰色和神经网络的铁路客运量预测研究
Research of railway passenger volu me forecast based on grey and neural network
摘要
Abstract
Accurate forecast for passenger volume plays an important role in the national traffic planning and management.The accuracy of prediction is directly influenced by its prediction technique.There are two features of the prediction of traffic volume:small samples and nonlinear.According to the characteristics of the grey theo-ry and RBF neural network,the grey RBF neural network model was formed in this paper.Moreover,the pas-senger volume was forecasted by virtue of the traffic share rate.The initial data was first generated and processed within the framework of grey theory to turn the erratic than the raw data into a regular sequence generation.By u-sing the high adaptability and learning ability of RBF neural network,which can greatly accelerate the learning speed and avoid the local minimum problem to predict the generated sequence.At last,the high prediction of the grey -RBF neural network model for passenger traffic volume will be illustrated by Lanzhou to Zhongchuan air-port railway new project and the survey data.关键词
客运量/预测/灰色理论/RBF 神经网络Key words
passenger traffic volume/prediction/grey theory/RBF neural network分类
交通工程引用本文复制引用
冯冰玉,鲍学英,王起才..基于灰色和神经网络的铁路客运量预测研究[J].铁道科学与工程学报,2015,(5):1227-1231,5.基金项目
长江学者和创新团队发展计划资助项目 （）
