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Perception-Based Road Traffic Congestion Classification Using Neural Networks and Decision Tree

  • Pitiphoom PosawangEmail author
  • Satidchoke Phosaard
  • Weerapong Polnigongit
  • Wasan Pattara-Atikom
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 60)

Abstract

In this study, we investigated an alternative technique to automatically classify road traffic congestion with travelers’ opinions. The method utilized an intelligent traffic camera system orchestrated with an interactive web survey system to collect the traffic conditions and travelers’ opinions. A large numbers of human perceptions were used to train the artificial neural network (ANN) model and the decision tree (J48) model that classify velocity and traffic flow into three congestion levels: light, heavy, and jam. The both model was then compared to the Occupancy Ratio (OR) technique, currently in service in the Bangkok Metropolitan Administration (BMA). The accuracy of ANN was more than accuracy of the J48. The evaluation indicated that our ANN model could determine the traffic congestion levels 12.15% more accurately than the existing system. The methodology, though conceived for use in Bangkok, is a general Intelligent Transportation System (ITS) practice that can be applied to any part of the world.

Keywords

Traffic congestion level determination intelligent transportation system (ITS) human judgment artificial neural network (ANN) decision tree (J48) occupancy ratio (OR) 

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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Pitiphoom Posawang
    • 1
    Email author
  • Satidchoke Phosaard
    • 1
  • Weerapong Polnigongit
    • 1
  • Wasan Pattara-Atikom
    • 2
  1. 1.School of Information TechnologySuranaree University of TechnologyNakhon RatchasimaThailand
  2. 2.National Electronics and Computer Technology Center (NECTEC), under the National Science and Technology Development Agency (NSTDA)Klong Luang, PathumthaniThailand

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