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YUAN Li-gang, HU Ming-hua, ZHANG Hong-hai, MA Yong. Phase-state identification of traffic flow in terminal area incorporated with prior experience clustering[J]. Journal of Traffic and Transportation Engineering, 2016, 16(5): 83-94. doi: 10.19818/j.cnki.1671-1637.2016.05.010
Citation: YUAN Li-gang, HU Ming-hua, ZHANG Hong-hai, MA Yong. Phase-state identification of traffic flow in terminal area incorporated with prior experience clustering[J]. Journal of Traffic and Transportation Engineering, 2016, 16(5): 83-94. doi: 10.19818/j.cnki.1671-1637.2016.05.010
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Phase-state identification of traffic flow in terminal area incorporated with prior experience clustering

doi: 10.19818/j.cnki.1671-1637.2016.05.010

  • School of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, Jiangsu, China

More Information
  • Author Bio:

    YUAN Li-gang(1980-), male, doctoral student, +86-25-52112039, kelfen_yuan@hotmail.com

    HU Ming-Hua(1962-), male, professor, +86-25-52112039, minghuahu@263.net

  • Received Date: 2016-04-21
  • Publish Date: 2016-10-25
    Abstract
  • The traffic flow in terminal area was taken as research object, and the characteristics of traffic flow were defined and extracted based on the result of trajectory spectral clustering.The relationship of characteristics and phase-state transition law of traffic flow were analyzed to reveal three phase-states of traffic flow under observed data, including free state, steady state and congestion state, which was regarded as prior experience to further design the identification method of traffic flow situation in terminal area combining factor analysis and fuzzy clustering algorithm of genetic expectation maximization, the influence factor of traffic flow state and the recessive characteristics of traffic flow were extracted, and the observed data from typical busy terminal area were chosen to do the verification.Analysis result shows that the identification method of traffic flow situation based on objective data mining has good adaptability and accuracy, the identification numbers by the method for free state, steady state and congestion state are 6, 36 and 37 respectively, the discrimination numbers by the controller are 7, 40 and 32 respectively, the error rates are 14.3%, 10.0% and 15.6% respectively, and the identification rates are all above 84%;the extracted phase-state and time-spatial characteristic of traffic flow can be used to structure the overall operation situation in terminal area from local detail, which can provide support for the time-spatial distribution allocation of flow in terminal area and theoptimization of arrival and departure procedure.

     

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