Fractal Characteristics of Mountain Cities' Traffic Flow Based on EMD and Multifractal

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Abstract:

Aim at the characteristics of the mountain cities road traffic network, the short-time data signals in the congestion state of the road network traffic is analyzed. Fractal characteristics of traffic data signal is in research based on the self-similarity of the traffic data signals. The non-stationary property of the traffic flow signal in the congestion state is known through the calculation of the multifractal spectrum of the traffic flow signal based on EMD. The experimental results show the feasibility of the method, which also can provide theoretical support for the traffic flow control of the mountain city road network in the sub-health state.

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430-434

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December 2013

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Houli D, Zhiheng L, Li L. Network-Wide Traffic State Observation and Analysis Method Using Pseudo-Color Map. Journal of Transportation Systems Engineering and Information Technology, 2009, 9(4):46-52.

DOI: 10.1016/s1570-6672(08)60071-9

Google Scholar

[2] Qi Hongsheng, Wang Dianhai, Song Xianmin. On the critical conditions of traffic jams [J]. Journal of Southeast University, 2011, 27(2): 180-184.

Google Scholar

[3] Qiliang R. Reasons and Alleviation Countermeasures on Urban Traffic Congestion in Chongqing. TRANSPORT STANDARDIZATION, 2009 (7): 163-167.

Google Scholar

[4] Darong H, Peng W, Bo Z. Reliability of ntworked-group systems based on information entropy. Control Theory & Applications, 2010, 29(2):177-182.

Google Scholar

[5] Ling Z, Darong H, Jun S. Fractal Characteristics of Mountain Cities' Traffic Flow with Sub-health State. Control Engineering of China, 2012, 19(4): 583-586.

Google Scholar

[6] Yueming C, Deyun X. Traffic network flow forecasting based on switching model. CONTROL AND DECISION. 2009, 24(8):1177-1180+118.

Google Scholar

[7] Pengjian Shang, Meng Wan, Santi Kama. Fractal nature of highway traffic data[J]. Computers and Mathematics with Applications, 2007(54): 107-116.

DOI: 10.1016/j.camwa.2006.07.017

Google Scholar

[8] Zhang J, Liu J. Nonlinear characteristics of short-term traffic flow and their influences to forecasting[C]. Proceedings of the IEEE International Conference on Automation and Logistics August 18-21, 2007, Jinan, China: 847-851.

DOI: 10.1109/ical.2007.4338682

Google Scholar

[9] Hong Z, Keqiang D., Multifractal analysis of traffic flow time series. Journal of Hebei University of Engineering(Natural Science Edition), 2009, 26(3):109-112.

Google Scholar

[10] Weixing Z, Yanjie W, Zunhong Y. Geometrical Characteristics of Singularity Spectra of Multifractals Ⅰ. Classical Definition. Journal of East China University of Science and Technology(Chemical and Biochemical Engineering Section), 2000, 26(4): 385—389.

Google Scholar

[11] Wei M, Ronghong J, Junping G. A Time-Frequency Analysis Method for Non-stationary Signals Based on Improved Hilbert-Huang Transform and Its Application. Journal of Shanghai Jiaotong Universiry. 2006, 40(5):724-729.

Google Scholar