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YU Bin, WU Shan-hua, WANG Ming-hua, ZHAO Zhi-hong. K-nearest neighbor model of short-term traffic flow forecast[J]. Journal of Traffic and Transportation Engineering, 2012, 12(2): 105-111. doi: 10.19818/j.cnki.1671-1637.2012.02.015
Citation: YU Bin, WU Shan-hua, WANG Ming-hua, ZHAO Zhi-hong. K-nearest neighbor model of short-term traffic flow forecast[J]. Journal of Traffic and Transportation Engineering, 2012, 12(2): 105-111. doi: 10.19818/j.cnki.1671-1637.2012.02.015
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K-nearest neighbor model of short-term traffic flow forecast

doi: 10.19818/j.cnki.1671-1637.2012.02.015
  • 1.

    School of Transportation Management, Dalian Maritime University, Dalian 116026, Liaoning, China

  • 2.

    School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

More Information
  • Author Bio:

    YU Bin (1977-), male, associate professor, PhD, +86-411-84726756, yubinyb@163.com

  • Received Date: 2011-10-29
  • Publish Date: 2012-04-25
    Abstract
  • In order to accurately forecast the short-term traffic flow, a K-nearest neighbor(K-NN) model was set up.The time and space parameters of the K-NN model were analyzed.Based on four different combinations of state vectors, the time dimension model, upstream section-time dimension model, downstream section-time dimension model and space-time dimension model were proposed.The four different models were validated by using the GPS data from taxis of Guiyang.Analysis result indicates that the K-NN model with both space and time parameters has highest forecasting precision than the other three models, and its average prediction error is about 7.26%.The distance measuring mode with exponent weight has higher accuracy in choosing the nearest neighbors, and its average prediction error is about 5.57%.The predicting performance of improved K-NN model with exponent weight and space-time parameters is best compared with the artificial neural network model and the historical average model, and its average prediction error is only 9.43%.So the improved K-NN model is an effective way for forecasting short-term traffic flow.

     

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