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Academic Session 3-B : International Conference Presentation(Ⅱ) 국제학술발표(Ⅱ)

Estimation of Annual Average Hourly Traffic Using Ensemble Machine Learning Algorithm : Focusing on Random Forest model

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영어

In this study, AAHT was estimated using machine learning algorithms of ensemble techniques to improve the limitations of existing studies. Among the machine learning algorithms, a random forest algorithm was used, and the traffic volume estimation accuracy was analyzed as MAPE 20.7%. It was analyzed that the higher the traffic volume level, the higher the traffic volume estimation accuracy, and the traffic volume level of 3,000 vehicles/hour or more was analyzed to be 8% or less of MAPE. It was analyzed that the accuracy secured at the current level was very high compared to the existing model.

저자정보

  • Seong-Min Kim Korea Transport Database Center Korea Transport Institute Sejong, South Korea
  • Seung-hoon Cheon Korea Transport Database Center Korea Transport Institute Sejong, South Korea
  • Chae-Young Lee Korea Transport Database Center Korea Transport Institute Sejong, South Korea

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