The Journal of The Korea Institute of Intelligent Transport Systems (한국ITS학회 논문지)

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
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Development of Traffic Speed Prediction Model Reflecting Spatio-temporal Impact based on Deep Neural Network

시공간적 영향력을 반영한 딥러닝 기반의 통행속도 예측 모형 개발

  • Kim, Youngchan (Dept. of Transportation Eng, Univ. of Seoul) ;
  • Kim, Junwon (Dept. of Transportation Eng, Univ. of Seoul) ;
  • Han, Yohee (Dept. of Transportation Eng, Univ. of Seoul) ;
  • Kim, Jongjun (Dept. of Transportation Eng, Univ. of Seoul) ;
  • Hwang, Jewoong (Dept. of Transportation Eng, Univ. of Seoul)
  • 김영찬 (서울시립대학교 교통공학과) ;
  • 김준원 (서울시립대학교 교통공학과) ;
  • 한여희 (서울시립대학교 교통공학과) ;
  • 김종준 (서울시립대학교 교통공학과) ;
  • 황제웅 (서울시립대학교 교통공학과)
  • Received : 2019.11.08
  • Accepted : 2020.01.09
  • Published : 2020.02.28
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Abstract

With the advent of the fourth industrial revolution era, there has been a growing interest in deep learning using big data, and studies using deep learning have been actively conducted in various fields. In the transportation sector, there are many advantages to using deep learning in research as much as using deep traffic big data. In this study, a short -term travel speed prediction model using LSTM, a deep learning technique, was constructed to predict the travel speed. The LSTM model suitable for time series prediction was selected considering that the travel speed data, which is used for prediction, is time series data. In order to predict the travel speed more precisely, we constructed a model that reflects both temporal and spatial effects. The model is a short-term prediction model that predicts after one hour. For the analysis data, the 5minute travel speed collected from the Seoul Transportation Information Center was used, and the analysis section was selected as a part of Gangnam where traffic was congested.

4차 산업혁명 시대가 도래함에 따라 빅데이터를 활용하는 딥러닝에 대한 관심이 높아졌으며 다양한 분야에서 딥러닝을 이용한 연구가 활발하게 진행되고 있다. 교통 분야에서도 교통빅데이터를 많이 활용하는 만큼 딥러닝을 연구에 이용한다면 많은 이점이 있을 것이다. 본 연구에서는 통행속도를 예측하기 위하여 딥러닝 기법인 LSTM을 이용한 단기 통행속도 예측 모형을 구축하였다. 예측에 활용한 데이터인 통행속도 데이터가 시계열 데이터인 것을 고려하여 시계열 예측에 적합한 LSTM 모델을 선택하였다. 통행속도를 보다 정확하게 예측하기 위하여 시간적, 공간적 영향을 모두 반영하는 모형을 구축하였으며, 모형은 1시간 이후를 예측하는 단기 예측모형이다. 분석데이터는 서울시 교통정보센터에서 수집한 5분 단위 통행속도를 활용하였고 분석구간은 교통이 혼잡한 강남대로 일부구간으로 선정하여 연구를 수행하였다.

Keywords

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