Traffic State Identification for Urban Expressway Based on Projection Pursuit Dynamic Cluster Model

BING Qichun; GONG Bowen; YANG Zhaosheng; LIN Ciyun; QU Xin

doi:10.3969/j.issn.0258-2724.2015.06.027

Volume 28 Issue 6

Dec. 2015

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Journal of Southwest Jiaotong University > 2015 > 28(6): 1164-1169.

BING Qichun, GONG Bowen, YANG Zhaosheng, LIN Ciyun, QU Xin. Traffic State Identification for Urban Expressway Based on Projection Pursuit Dynamic Cluster Model[J]. Journal of Southwest Jiaotong University, 2015, 28(6): 1164-1169. doi: 10.3969/j.issn.0258-2724.2015.06.027

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BING Qichun, GONG Bowen, YANG Zhaosheng, LIN Ciyun, QU Xin. Traffic State Identification for Urban Expressway Based on Projection Pursuit Dynamic Cluster Model[J]. Journal of Southwest Jiaotong University, 2015, 28(6): 1164-1169. doi: 10.3969/j.issn.0258-2724.2015.06.027

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Traffic State Identification for Urban Expressway Based on Projection Pursuit Dynamic Cluster Model

doi: 10.3969/j.issn.0258-2724.2015.06.027

BING Qichun¹,
GONG Bowen^{1,2,3
,
,},
YANG Zhaosheng^1,2,3,
LIN Ciyun^1,2,3,
QU Xin¹

Received Date: 09 Jan 2015
Publish Date: 25 Dec 2015

Abstract

Abstract

In order to improve the accuracy of traffic state identification for urban expressway based on the spot traffic parameters, a traffic state identification method based on projection pursuit dynamic cluster model was proposed using the mapping relationship between spot traffic parameters and traffic state. First, the projection index function was constructed by combined use of the projection pursuit technology and the dynamic cluster method, and the shuffled frog leaping algorithm was used to optimize the projection direction. Then, the traffic state identification threshold was determined using simulation data. Finally, validation and comparative analysis were carried out using both the simulated data and measured data. Experimental results indicate that the proposed model can effectively improve the accuracy of traffic state identification. The average identification rate is 97.01% and the average false identification rate is 0.86%. The average identification accuracy of proposed method is 8.9% and 4.5% higher than the BP neural network model and the fuzzy C-means clustering model, respectively.
- traffic state identification,
- projection pursuit dynamic cluster,
- shuffled frog leaping algorithm,
- urban expressway

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References(17)

References

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Traffic State Identification for Urban Expressway Based on Projection Pursuit Dynamic Cluster Model

doi: 10.3969/j.issn.0258-2724.2015.06.027