A Short-Term Vehicle Speed Prediction using Bayesian Network Based Selective Data Learning

선별적 데이터 학습 기반의 베이지안 네트워크를 이용한 단기차량속도 예측

  • Received : 2015.08.10
  • Accepted : 2015.09.11
  • Published : 2015.12.31


The prediction of the accurate traffic information can provide an optimal route from the place of departure to a destination, therefore, this makes it possible to obtain a saving of time and money. To predict traffic information, we use a Bayesian network method based on probability model in this paper. Existing researches predicting the traffic information based on a Bayesian network generally used to study the data for all time. In this paper, however, only data corresponding to same time and day of the week to predict selectively will be used for learning. In fact, the experiment was carried out for 14 links zone in Seoul, also, the accuracy of the prediction results of the two different methods should be tested with MAPE (Mean Absolute Percentage Error) which is commonly used. In view of MAPE, experimental results show that the proposed method may calculate traffic prediction value with a higher accuracy than the method used to learn the data for all time zones.


Short-term Vehicle Speed Prediction;Urban Road;Bayesian Network;Selective Data Learning


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Supported by : Ministry of Land, Infrastructure and Transport Affairs