A Study of Travel Time Prediction using K-Nearest Neighborhood Method

K 최대근접이웃 방법을 이용한 통행시간 예측에 대한 연구

  • Lim, Sung-Han (Highway & Transportation Research Division, Korea Institute of Construction Technology) ;
  • Lee, Hyang-Mi (Highway & Transportation Research Division, Korea Institute of Construction Technology) ;
  • Park, Seong-Lyong (ITS & Road Environment Division, Ministry of Land, Infrastructure and Transport) ;
  • Heo, Tae-Young (Department of Information and Statistics, Chungbuk National University)
  • 임성한 (한국건설기술연구원 도로교통연구실) ;
  • 이향미 (한국건설기술연구원 도로교통연구실) ;
  • 박성룡 (국토교통부 첨단도로환경과) ;
  • 허태영 (충북대학교 자연과학대학 정보통계학과)
  • Received : 2013.09.06
  • Accepted : 2013.10.15
  • Published : 2013.10.31


Travel-time is considered the most typical and preferred traffic information for intelligent transportation systems(ITS). This paper proposes a real-time travel-time prediction method for a national highway. In this paper, the K-nearest neighbor(KNN) method is used for travel time prediction. The KNN method (a nonparametric method) is appropriate for a real-time traffic management system because the method needs no additional assumptions or parameter calibration. The performances of various models are compared based on mean absolute percentage error(MAPE) and coefficient of variation(CV). In real application, the analysis of real traffic data collected from Korean national highways indicates that the proposed model outperforms other prediction models such as the historical average model and the Kalman filter model. It is expected to improve travel-time reliability by flexibly using travel-time from the proposed model with travel-time from the interval detectors.


Travel-time prediction;nonparametric method;K-nearest neighbor;intelligent transportation system


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