Analysis and Short-Term Forecasting of Highway Traffic Flow in Slovenia

  • Primož Potočnik
  • Edvard Govekar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6593)


Analysis and short-term forecasting of traffic flow data for several locations of the Slovenia highway network are presented. Daily and weekly seasonal components of the data are analysed and several features are extracted to support the forecasting. Various short-term forecasting models are developed for one hour ahead forecasting of the traffic flow. Models include benchmark models (random walk, seasonal random walk, naive model), AR and ARMA models, and various configuration of feedforward neural networks. Results show that the best forecasting results (correlation coefficient R > 0.99) are obtained by a feedforward neural network and a selected set of inputs but this sophisticated model surprisingly only slightly surpasses the accuracy of a simple naive model.


traffic flow analysis forecasting neural networks 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Primož Potočnik
    • 1
  • Edvard Govekar
    • 1
  1. 1.Faculty of Mechanical EngineeringUniversity of LjubljanaSlovenia

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