摘要
在分析径向基神经网络原理和铁路客流时序特征的基础上,建立基于径向基神经网络的铁路短期客流预测模型,通过径向基神经网络把客运量的年规律、周规律等时间属性有机结合,有效解决客流数据的复杂性和非线性问题。以T15次列车为例进行硬座席别的客运量预测结果表明,径向基神经网络预测模型对铁路短期客流的预测效果较好。
Based on analyzing the principle of RBF neural network and time sequence characteristics of railway passenger flow,the forecast model of railway short-term passenger flow based on RBF neural network was established,through the network,the time properties of passenger traffic volume like annual rule,weekend rule were combined,which availably resolve the complexity and non-linearity problems of passenger flow data.Take T15 train as example,the forecast of passenger traffic volume in seat level was taken,and the result shows the forecast model based on RBF neural network has good forecast effect on railway short-term passenger flow.
出处
《铁道运输与经济》
北大核心
2011年第6期86-89,共4页
Railway Transport and Economy
基金
铁道部科技开发计划(2009F019)
关键词
铁路
客流预测
客运量
径向基神经网络
Railway
Forecast of Passenger Flow
Passenger Traffic Volume
RBF Neural Network
