Elman neural network model of freeway dynamic traffic flow

LIANG Xin-rong; LIU Zhi-yong; MAO Zong-yuan

Volume 6 Issue 3

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LIANG Xin-rong, LIU Zhi-yong, MAO Zong-yuan. Elman neural network model of freeway dynamic traffic flow[J]. Journal of Traffic and Transportation Engineering, 2006, 6(3): 92-96.

Citation:

LIANG Xin-rong, LIU Zhi-yong, MAO Zong-yuan. Elman neural network model of freeway dynamic traffic flow[J]. Journal of Traffic and Transportation Engineering, 2006, 6(3): 92-96.

LIANG Xin-rong, LIU Zhi-yong, MAO Zong-yuan. Elman neural network model of freeway dynamic traffic flow[J]. Journal of Traffic and Transportation Engineering, 2006, 6(3): 92-96.

Citation:

LIANG Xin-rong, LIU Zhi-yong, MAO Zong-yuan. Elman neural network model of freeway dynamic traffic flow[J]. Journal of Traffic and Transportation Engineering, 2006, 6(3): 92-96.

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Elman neural network model of freeway dynamic traffic flow

1.
School of Automation, South China University of Technology, Guangzhou 510640, Guangdong, China
2.
School of Information, Wuyi University, Jiangmen 529020, Guangdong, China

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Author Bio:
Liang Xin-rong(1964-), male, PhD, associate professor, 86-750-3299874, xrlian955@126.com
Received Date: 2005-12-06
Publish Date: 2006-09-25

Abstract

Abstract

In order to improve the accuracy of freeway traffic flow modeling, the discrete mathematical model of freeway dynamic traffic flow was analyzed, and a traffic flow model of recurrent neural network was built based on the principle of Elman network. The node numbers of the input layer, context layer, hidden layer and output layer of the recurrent network were selected as 8, 30, 30 and 2 respectively. Levenberg-Marquardt algorithm was used to train the recurrent network, and a freeway with five segments was simulated. Simulation result shows that the average relative error and the maximum relative error for the recurrent network are 8. 683 7 × 10^-5 and 4. 237 1 × 10^-4 respectively, compared with the BP and RBF neural network, the Elman recurrent network can approach the mathematical model of freeway traffic flow more accurately, can better describe the basic properties of traffic flow, and by means of on-line learning from the data measured by sensors on the freeway, the Elman recurrent network can adapt to the change of traffic status. 1 tab, 7 figs, 10 refs.
- traffic planning,
- dynamic traffic flow,
- recurrent neural network,
- modeling,
- comparing

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References(10)

References

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