LISS 2012 pp 1217-1222 | Cite as

A Knowledge-Based Fast Recognition Method of Urban Traffic Flow States

Conference paper

Abstract

A Fast Recognition method of urban traffic flow states based on knowledge was put forward. Rough sets theory were used to express traffic flow parameter—traffic states and their relationship, and the traffic flow states recognition knowledge base was established based on knowledge model. Supported by above traffic flow states knowledge discovery model and knowledge base, a recognition algorithm of real-time traffic flow states based on knowledge was presented. Finally, an example is presented to illustrate the effectiveness of the proposed method.

Keywords

Rough sets Knowledge discovery model Knowledge base Traffic flow states recognition 

Notes

Acknowledgments

This research is supported by Humanities and Social Sciences Foundation of Education Ministry of China (Grant No. 11YJC630195).

References

  1. 1.
    Helbing D (ed) (2002) Micro and macro-simulation of freeway traffic. Math Comput Model 35:547Google Scholar
  2. 2.
    Kerner BS (2004) Three-phase traffic theory and highway capacity. Phys A Stat Mech Appl 333:379–440CrossRefGoogle Scholar
  3. 3.
    Treiber MA, Kesting D (2006) Delays, inaccuracies and anticipation in microscopic traffic models. Phys A Stat Mech Appl 360:71–88CrossRefGoogle Scholar
  4. 4.
    Wang CD (2005) Classification of traffic flow situation of urban freeways based on fuzzy clustering. J Transp Syst Eng Inf 5:62–67Google Scholar
  5. 5.
    Adel WS, Michael JD, Brain LS (1999) Case-based reasoning for real-time traffic flow management. Comput Aided Civ Infrastruct Eng 14:347–356CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.China Center for Industrial Security ResearchBeijing Jiaotong UniversityBeijingP.R. China
  2. 2.Research Center for Beijing Industrial Security and DevelopmentBeijingP.R. China

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