Traffic Flow Prediction Based on Combined Model of ARIMA and RBF Neural Network

被引：0

作者：

Wang Yuqiong ^{[1
]}

机构：

[1] Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing 100044, Peoples R China

来源：

PROCEEDINGS OF THE 2017 2ND INTERNATIONAL CONFERENCE ON MACHINERY, ELECTRONICS AND CONTROL SIMULATION (MECS 2017) | 2017年 / 138卷

关键词：

traffic engineering; traffic flow prediction; Combined Model; ARIMA model; RBF neural network model;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this paper, a combined model of ARIMA and RBF neural network is proposed by combined the good linear fit ability of ARIMA and the strong dynamic nonlinear mapping ability of RBF neural network. The velocity of microwave is predicted in real time with the consideration of the temporal characteristics of traffic flow by the models. The results indicate that the Mean Absolute Percentage Error of combined model is lower, and the goodness of fit of combined model is higher.

引用

页码：82 / 86

页数：5

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