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Classification of Road Traffic Congestion Levels from Vehicle’s Moving Patterns: A Comparison Between Artificial Neural Network and Decision Tree Algorithm

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

Abstract

We proposed a technique to identify road traffic congestion levels from velocity of mobile sensors with high accuracy and consistent with motorists’ judgments. The data collection utilized a GPS device, a webcam, and an opinion survey. Human perceptions were used to rate the traffic congestion levels into three levels: light, heavy, and jam. We successfully extracted vehicle’s moving patterns using a sliding windows technique. Then the moving patterns were fed into ANN and J48 algorithms. The comparison between two learning algorithms yielded that the J48 model shown the best result which achieved accuracy as high as 91.29%. By implementing the model on the existing traffic report systems, the reports will cover on comprehensive areas. The proposed method can be applied to any parts of the world.

Keywords

Intelligent transportation system (ITS) traffic congestion level human judgment artificial neural network (ANN) decision tree (J48) GPS sliding windows moving pattern 

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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Thammasak Thianniwet
    • 1
    Email author
  • Satidchoke Phosaard
    • 1
  • Wasan Pattara-Atikom
    • 2
  1. 1.School of Information Technology, Institute of Social TechnologySuranaree University of TechnologyNakhon RatchasimaThailand
  2. 2.National Electronics and Computer Technology Center (NECTEC), Under the National Science and Technology Development Agency (NSTDA), Ministry of Science and TechnologyKlong Luang, PathumthaniThailand

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