Traffic congestion identification method of urban expressway

JIANG Gui-yan; GANG Long-hui; WANG Jiang-feng

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JIANG Gui-yan, GANG Long-hui, WANG Jiang-feng. Traffic congestion identification method of urban expressway[J]. Journal of Traffic and Transportation Engineering, 2006, 6(3): 87-91.

Citation:

JIANG Gui-yan, GANG Long-hui, WANG Jiang-feng. Traffic congestion identification method of urban expressway[J]. Journal of Traffic and Transportation Engineering, 2006, 6(3): 87-91.

JIANG Gui-yan, GANG Long-hui, WANG Jiang-feng. Traffic congestion identification method of urban expressway[J]. Journal of Traffic and Transportation Engineering, 2006, 6(3): 87-91.

Citation:

JIANG Gui-yan, GANG Long-hui, WANG Jiang-feng. Traffic congestion identification method of urban expressway[J]. Journal of Traffic and Transportation Engineering, 2006, 6(3): 87-91.

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Traffic congestion identification method of urban expressway

School of Transportation, Jilin University, Changchun 130025, Jilin, China

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Author Bio:
Jiang Gui-yan(1964-), female, professor, 86-431-5095505, jianggy@public.cc.il.cn
Received Date: 2005-11-20
Publish Date: 2006-09-25

Abstract

Abstract

In order to quickly identify traffic congestion from mass dynamic traffic information, traffic congestion pattern and the characteristics of various data mining technologies were analyzed, an auto-identifying method of urban expressway traffic congestion was designed.The flow, speed and occupancy of expressway were combined into several new eigenvectors, optimized multi-layer feedforward perceptron model was adopted to classify the eigenvectors during congestion and non-congestion, recurrent congestion and non-recurrent congestion could be distinguished by analyzing the variances of the model outputs, the method was tested with simulated data and actual data from an urban expressway. The result shows that the method has great practicability and can identify congestion states on urban expressway correctly.
- traffic information engineering,
- traffic congestion identification,
- data mining,
- artificial neural network,
- traffic state

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References(11)

References

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