Traffic Congestion Estimation by Adopting Recurrent Neural Network

순환인공신경망(RNN)을 이용한 대도시 도심부 교통혼잡 예측

  • Jung, Hee jin (Super Computing Center, Korea Institute of Science and Technology Information) ;
  • Yoon, Jin su (National Transport Technology R&D Center, Korea Transport Institute) ;
  • Bae, Sang hoon (Dept. of Spatial Information Engineering, Pukyong National Univ.)
  • 정희진 (한국과학기술정보연구원 슈퍼컴퓨팅본부) ;
  • 윤진수 (한국교통연구원 교통기술연구소) ;
  • 배상훈 (부경대학교 공간정보시스템공학과)
  • Received : 2017.05.24
  • Accepted : 2017.12.08
  • Published : 2017.12.31


Traffic congestion cost is increasing annually. Specifically congestion caused by the CDB traffic contains more than a half of the total congestion cost. Recent advancement in the field of Big Data, AI paved the way to industry revolution 4.0. And, these new technologies creates tremendous changes in the traffic information dissemination. Eventually, accurate and timely traffic information will give a positive impact on decreasing traffic congestion cost. This study, therefore, focused on developing both recurrent and non-recurrent congestion prediction models on urban roads by adopting Recurrent Neural Network(RNN), a tribe in machine learning. Two hidden layers with scaled conjugate gradient backpropagation algorithm were selected, and tested. Result of the analysis driven the authors to 25 meaningful links out of 33 total links that have appropriate mean square errors. Authors concluded that RNN model is a feasible model to predict congestion.


Supported by : 부경대학교


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