基于Verhulst–RBF的铁路客运量预测  

Prediction of railway passenger traffic volume based on Verhulst–RBF

作　　者：付洁 黄洪[1] FU Jie;HUANG Hong(School of Information Science&Technology,Southwest Jiaotong University,Chengdu 611756,China)

机构地区：[1]西南交通大学信息科学与技术学院

出　　处：《铁路计算机应用》2019年第11期1-4,17,共5页Railway Computer Application

摘　　要：为更准确地预测铁路客运量,采用灰色关联法,分析不同因素对铁路客运量的影响程度,确定主要影响因子,并将其作为预测指标,提出基于Verhulst–RBF神经网络的铁路客运量预测组合模型。基于四川省近14年的铁路客运量数据,进行组合模型测试。实验结果表明,Verhulst–RBF神经网络组合模型的预测精确度高于单一的Verhulst模型或单一的RBF神经网络模型。In order to predict the railway passenger traffic volume more accurately, this paper used the grey correlation method to analyze the influence degree of different factors on the railway passenger traffic volume, determine the main influencing factors, and took them as the prediction indexes to propose a combined predicting model of railway passenger traffic volume based on Verhulst–RBF neural network.Based on the railway passenger traffic volume data of Sichuan Province in recent 14 years, the combined model was tested.The experimental results show that the prediction accuracy of Verhulst–RBF neural network combined model is higher than that of single Verhulst model or single RBF neural network model.

关 键 词：灰色关联 组合模型 RBF神经网络 铁路客运量 VERHULST模型 

分 类 号：U293[交通运输工程—交通运输规划与管理] TP39[交通运输工程—道路与铁道工程]