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http://dx.doi.org/10.6109/JKIICE.2009.13.4.765

A Short-Term Traffic Information Prediction Model Using Bayesian Network  

Yu, Young-Jung (부산외국어대학교 컴퓨터공학과)
Cho, Mi-Gyung (동명대학교 멀티미디어공학과)
Abstract
Currently Telematics traffic information services have been various because we can collect real-time traffic information through Intelligent Transport System. In this paper, we proposed and implemented a short-term traffic information prediction model for giving to guarantee the traffic information with high quality in the near future. A Short-term prediction model is for forecasting traffic flows of each segment in the near future. Our prediction model gives an average speed on the each segment from 5 minutes later to 60 minutes later. We designed a Bayesian network for each segment with some casual nodes which makes an impact to the road situation in the future and found out its joint probability density function on the supposition of GMM(Gaussian Mixture Model) using EM(Expectation Maximization) algorithm with training real-time traffic data. To validate the precision of our prediction model we had conducted various experiments with real-time traffic data and computed RMSE(Root Mean Square Error) between a real speed and its prediction speed. As the result, our model gave 4.5, 4.8, 5.2 as an average value of RMSE about 10, 30, 60 minutes later, respectively.
Keywords
지능형교통정보 시스템;텔레매틱스;교통정보 예측;베이지안 네트워크;예측모델;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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