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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2014  >  14(3): 87-94
XIE Hai-hong, DAI Xu-hao, QI Yuan. Improved K-nearest neighbor algorithm for short-term traffic flow forecasting[J]. Journal of Traffic and Transportation Engineering, 2014, 14(3): 87-94.
Citation: XIE Hai-hong, DAI Xu-hao, QI Yuan. Improved K-nearest neighbor algorithm for short-term traffic flow forecasting[J]. Journal of Traffic and Transportation Engineering, 2014, 14(3): 87-94.
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Improved K-nearest neighbor algorithm for short-term traffic flow forecasting

  • 1.

    MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China

  • 2.

    Center of Cooperative Innovation for Beijing Metropolitan Transportation, Beijing 100044, China

  • 3.

    Hunan Provincial Communications Planning, Survey and Design Institute, Changsha 410008, Hunan, China

More Information
  • Author Bio:

    XIE Hai-hong (1963-), female, associate professor, +86-10-51687138, xiehaihong16@163.com

  • Received Date: 2014-01-13
  • Publish Date: 2014-06-25
    Abstract
  • The original K-nearest neighbor algorithm for short-term traffic flow forecasting was analyzed.Pattern distance search method was used to replace the original Euclidean distance search method, the multiple statistics regression model was introduced, an improved K-nearest neighbor algorithm for short-term traffic flow forecasting was put forward, and an example verification was carried out by using the traffic flow data from a certain section in Beijing.Test result indicates when Kis 23, the error of mean square, mean absolute error and average relative error of forecasting results are 31.43%, 4.17% and 0.27% respectively by using the improved K-nearest neighbor algorithm.By using the original K-nearest neighbor algorithm, the error of mean square, mean absolute error and average relative error of forecasting results are 33.33%, 4.40% and 0.28% respectively.By using the historical average model, the error of mean square, mean absolute error and average relative error of forecasting results are 46.20%, 11.40% and 0.48% respectively.The forecasting accuracy of the improved K-nearest neighbor algorithm is obviously higher than the other two algorithms.The improved K-nearest neighbor algorithm notonly increases searching efficiency, but also accurately reflects the real situation of traffic flow.

     

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