Research on the Combination Model of Short-Term Traffic Flow Forecasting

被引：0
|
作者：
Liu Yuanlin [1 ]
Hu Wusheng [1 ]
Li Sulan [2 ]
Li Hongwei [1 ]
机构：
[1] Southeast Univ, Sch Transportat, Nanjing 210096, Jiangsu, Peoples R China
[2] Bridge Maintenance Management Off Wuhan, Wuhan 430015, Peoples R China
来源：
SUSTAINABLE ENVIRONMENT AND TRANSPORTATION, PTS 1-4 | 2012年 / 178-181卷
基金：
国家高技术研究发展计划(863计划);
关键词：
Multiple Linear Regression; BP Neural Network; Short-term Traffic Flow; Model; Input Layer Elements;
D O I：
10.4028/www.scientific.net/AMM.178-181.2668
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
Short-term traffic flow is difficult to predict accurately and real-time, owing to the characteristics of very complexity, randomness, nonlinearity and uncertainty, etc.. In this paper, the method of combining multiple linear regression with back propagation (BP) neural network was proposed, using BP neural network to compensate the model error of multiple linear regression. The combination model and the corresponding algorithm program was made, and used to pedict the short-term traffic flow. Two different methods of selecting the input layer parameters were used and compared, while the new method has higher accuracy and stability.
引用
收藏
页码：2668 / +
页数：2
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