基于RBF神经网络的城市快速路短时交通流预测研究

doi:10.3976/j.issn.1002-4026.2012.03.005

J4 ›› 2012, Vol. 25 ›› Issue (3): 23-28.doi: 10.3976/j.issn.1002-4026.2012.03.005

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RBF neural network based urban expressway short-term traffic flow prediction

ZHENG Xuan-Chuan¹, HAN Bao-Ming¹, LI De-Wei²

1.School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China;
2.State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China

Received:2012-03-27 Online:2012-06-20 Published:2012-06-20

Abstract

Abstract:

We analyzed and compared the effects of gray model GM (1,1) and RBF neural network model on short-term traffic flow prediction to test their feasibility and applicability. Practical instances show that gray model is inapplicable to the short-term prediction of traffic flow, but RBF neural network model is applicable. Moreover, we can acquire higher prediction accuracy when the distribution density of the radial basis function is from 0.8 to 1.0.

Key words: traffic flow, prediction, gray model, RBF neural network

CLC Number:

U491.1⁺12

ZHENG Xuan-Chuan, HAN Bao-Ming, LI De-Wei. RBF neural network based urban expressway short-term traffic flow prediction[J].J4, 2012, 25(3): 23-28.

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RBF neural network based urban expressway short-term traffic flow prediction

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