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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2010  >  10(5): 114-120
ZHANG Jing, JU Yong-feng, CHEN Li. Detection method of traffic state for urban traffic network based on wavelet analysis[J]. Journal of Traffic and Transportation Engineering, 2010, 10(5): 114-120. doi: 10.19818/j.cnki.1671-1637.2010.05.020
Citation: ZHANG Jing, JU Yong-feng, CHEN Li. Detection method of traffic state for urban traffic network based on wavelet analysis[J]. Journal of Traffic and Transportation Engineering, 2010, 10(5): 114-120. doi: 10.19818/j.cnki.1671-1637.2010.05.020
shu

Detection method of traffic state for urban traffic network based on wavelet analysis

doi: 10.19818/j.cnki.1671-1637.2010.05.020
  • 1.

    School of Electronic and Control Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    School of Highway, Chang'an University, Xi'an 710064, Shaanxi, China

  • 3.

    Educational Technology and Network Center, Chang'an University, Xi'an 710064, Shaanxi, China

More Information
  • Author Bio:

    ZHANG Jing(1972-), female, lecturer, doctoral student, +86-29-82339626, jingzhang@chd.edu.cn

    JU Yong-feng(1962-), male, professor, PhD, +86-29-82334555, yfju@chd.edu.cn

  • Received Date: 2010-05-24
  • Publish Date: 2010-10-25
    Abstract
  • The import saturation degree of intersection and the average travel speed of road section were selected as the basic parameters of road network's state detection, the high time-frequency properties of wavelet packet transform was adopted, and the mutation and unusual conditions of the saturation and the speed were distinguished by using energy analysis method. In order to describe the change of traffic state, a coefficient was defined, a traffic state detection algorithm was designed by using wavelet analysis, and Bayesian Method was used to predict the traffic state. Simulation result shows that the mutation interval of energy distribution can be identified by using wavelet analysis, based on which the changing time interval of traffic state can be distinguished. While the maximal points of sampling data modulus are from 200 to 243, the energy change of the section node is intense, and the state changes from steadiness to unsteadiness. When the coefficient of traffic state is more than 0.300 h·km-1, a crowded state appears. The method with simple working principle and short time response to congestion is feasible because of its credible detection result.

     

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