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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:

27

期数:

2017年01期

页码:

169-172

栏目:

应用开发研究

出版日期:

2017-01-10

文章信息/Info

Title:

 Traffic Flow Prediction Based on Hybrid Model of ARIMA and WNN

文章编号:

1673-629X（2017)01-0169-04

作者:

 成云; 成孝刚; 谈苗苗; 周凯; 李海波

 南京邮电大学 通信与信息工程学院

Author(s):

 CHENG Yun；CHENG Xiao-gang；TAN Miao-miao；ZHOU Kai；LI Hai-bo

关键词:

 ; 交通流预测; 差分自回归滑动平均模型; 小波神经网络; 组合模型

Keywords:

 ; traffic flow prediction; ARIMA model; wavelet neural network; hybrid model

分类号:

U491112

文献标志码:

A

摘要:

 针对现阶段城市道路交通流预测精度不高的局限性，提出了一种基于差分自回归滑动平均( ARIMA)和小波神经网络( WNN)组合模型的预测方法来进行交通流预测。利用差分自回归滑动平均模型良好的线性拟合能力和小波神经网络模型强大的非线性关系映射能力，把交通流时间序列的数据结构分解为线性自相关结构和非线性结构两部分。采用差分自回归滑动平均模型预测交通流序列的线性部分，用小波神经网络模型预测其非线性残差部分，最终合成为整个交通流序列的预测结果。计算机仿真结果表明：组合模型的预测精度高于ARIMA模型和WNN模型各自单独使用时的预测精度，组合模型可以提高交通流预测精度，是交通流预测的有效方法。

Abstract:

 Aimed at the limitation of low prediction accuracy at the present stage of city road traffic,a prediction method is proposed based on Hybrid Autoregressive Integrated Moving Average ( ARIMA) and Wavelet Neural Network ( WNN) to predict traffic flow. Using the good linear fitting ability of ARIMA and the strong nonlinear mapping ability of WNN,the traffic flow time series are considered to be composed of a linear autocorrelation structure and a nonlinear structure. ARIMA model is used to predict the linear component of traffic flow time series and the wavelet neural network model is applied to the nonlinear residual component prediction. The simulation results show that the hybrid model can produce more accurate prediction than that of single model,which improves prediction accuracy of traffic flow prediction,and it’ s an efficient method.

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更新日期/Last Update: 2017-04-05