An improved method of short-term traffic prediction

被引:0
|
作者
Hongfei, J [1 ]
Ming, T [1 ]
Zhongxiang, H [1 ]
Xiaoxiong, Z [1 ]
机构
[1] Jilin Univ, Transportat Coll, Changchun, Jilin, Peoples R China
来源
URBAN TRANSPORT XI: URBAN TRANSPORT AND THE ENVIRONMENT IN THE 21ST CENTURY | 2005年
关键词
short-term traffic prediction; prognosis horizon; chaos; ANN (Artificial Neural Network); fractal self-similar model;
D O I
暂无
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
Traffic congestion is one of the most severely disturbing problems of everyday life in Metropolitan areas. An urban traffic network is at its most vulnerable during peak hours and slight fluctuations of capacity may cause severe congestion. An effective way to avoid this phenomenon is to predict and forestall congestion. Therefore the development of an accurate short-term traffic prediction method could have very real and substantial practical benefits by means of linking it with the urban traffic network control system. In this paper, an improved method is proposed which combines the ANN (Artificial Neural Network) model (used for matching the linear trends) with the firactal self-similar model (used for matching the non-linear trends), and sets proper coefficients of the two results with the real data and real-time. Result shows that the improved method can provide a more accurate prediction for short-term traffic flow in an urban area.
引用
收藏
页码:649 / 658
页数:10
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