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The Traffic Flow Prediction Using Bayesian and Neural Networks

  • Teresa PamułaEmail author
  • Aleksander Król
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 32)

Abstract

The article presents two short-term forecasting models for determining the traffic flow volumes. The road traffic characteristics are essential for identification the trends in the distribution of the road traffic in the network, determination the capacity of the roads and the traffic variability over the time. The presented model is based on the historical, detailed data concerning the road traffic. The aim of the study was to compare the short-term forecasting models based on Bayesian networks (BN) and artificial neural networks (NN), which can be used in traffic control systems especially incorporated into modules of Intelligent Transportation Systems (ITS). Additionally the comparison with forecasts provided by the Bayesian Dynamic Linear Model (DLM) was performed. The results of the research shows that artificial intelligence methods can be successfully used in traffic management systems.

Keywords

Root Mean Square Error Traffic Flow Road Traffic Mean Absolute Percentage Error Error Indicator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of TransportSilesian University of TechnologyKatowicePoland

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