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An algorithm for the recognition of levels of congestion in road traffic problems

Author

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  • Lozano, Angélica
  • Manfredi, Giuseppe
  • Nieddu, Luciano

Abstract

Detection and recognition of the level of congestion at an intersection is a very important problem and a valuable source of information in traffic management. Although it is just one of all the aspects that make up a traffic management system, it seems to be a crucial point for gathering information. In this paper, we present a technique based on a k-means clustering algorithm for classification, which has been already successfully used in a number of pattern recognition problems, namely: as an algorithm for face recognition problems and in a number of medical diagnosis problems and it compares very well with the state of the art techniques.

Suggested Citation

  • Lozano, Angélica & Manfredi, Giuseppe & Nieddu, Luciano, 2009. "An algorithm for the recognition of levels of congestion in road traffic problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(6), pages 1926-1934.
    • Handle: RePEc:eee:matcom:v:79:y:2009:i:6:p:1926-1934
    DOI: 10.1016/j.matcom.2007.06.008
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    Cited by:

    1. Cheng, Zeyang & Wang, Wei & Lu, Jian & Xing, Xue, 2020. "Classifying the traffic state of urban expressways: A machine-learning approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 411-428.

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