[1] Zhao Zehui, Kang Haigui, Li Mingwei,. Expressway traffic flow predictionusing chaos cloud particle swarm algorithm and PPPR model [J]. Journal of Southeast University (English Edition), 2013, 29 (3): 328-335. [doi:10.3969/j.issn.1003-7985.2013.03.018]

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Expressway traffic flow predictionusing chaos cloud particle swarm algorithm and PPPR model()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

29

Issue:

2013 3

Page:

328-335

Research Field:

Traffic and Transportation Engineering

Publishing date:

2013-09-20

Info

Title:

Expressway traffic flow predictionusing chaos cloud particle swarm algorithm and PPPR model

Author(s):

Zhao Zehui;  Kang Haigui;  Li Mingwei

Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China

Keywords:

expressway traffic flow forecasting;  projection pursuit regression;  particle swarm algorithm;  chaotic mapping;  cloud model

PACS:

U491

DOI:

10.3969/j.issn.1003-7985.2013.03.018

Abstract:

Aiming at the real-time, fluctuation and nonlinear characteristics of the expressway short-term traffic flow forecasting, the parameter projection pursuit regression(PPPR)model is applied to forecast the expressway traffic flow, where the orthogonal Hermite polynomial is used to fit the ridge functions and the least square method is employed to determine the polynomial weight coefficient c. In order to efficiently optimize the projection direction a and the number M of ridge functions of the PPPR model, the chaos cloud particle swarm optimization(CCPSO)algorithm is applied to optimize the parameters. The CCPSO-PPPR hybrid optimization model for expressway short-term traffic flow forecasting is established, in which the CCPSO algorithm is used to optimize the optimal projection direction a in the inner layer while the number M of ridge functions is optimized in the outer layer. Traffic volume, weather factors and travel date of the previous several time intervals of the road section are taken as the input influencing factors. Example forecasting and model comparison results indicate that the proposed model can obtain a better forecasting effect and its absolute error is controlled within ［-6, 6］, which can meet the application requirements of expressway traffic flow forecasting.

References:

[1] Han Chao. Real-time short-term traffic flow adaptive forecasting method based on time series analysis[D]. Beijing: School of Electronic Information and Control Engineering of Beijing University of Technology, 2004: 41-51.(in Chinese)
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[11] Sheng Yuxiang, Pan Haitian, Xia Luyue, et al. Hybrid chaos particle swarm optimization algorithm and application in benzene-toluene flash vaporization[J]. Journal of Zhejiang University of Technology, 2010, 38(3): 319-322.(in Chinese)
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Memo

Memo:

Biographies: Zhao Zehui(1974—), male, graduate; Kang Haigui(corresponding author), male, professor, hgkang@dlut.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.71101014, 50679008), Specialized Research Fund for the Doctoral Program of Higher Education(No.200801411105), the Science and Technology Project of the Department of Communications of Henan Province(No.2010D107-4).
Citation: Zhao Zehui, Kang Haigui, Li Mingwei. Expressway traffic flow prediction using chaos cloud particle swarm algorithm and PPPR model[J].Journal of Southeast University(English Edition), 2013, 29(3):328-335.[doi:10.3969/j.issn.1003-7985.2013.03.018]

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