NDLI: A Short-Term Prediction Model for Forecasting Traffic Information Using Bayesian Network

Content Provider	IEEE Xplore Digital Library
Author	Young Jung Yu Mi-Gyung Cho
Copyright Year	2008
Description	Author affiliation: Dept. of Multimedia Eng., Tongmyong Univ., Busan (Mi-Gyung Cho) \|\| Div. of Comput. Eng., Pusan Univ. of Foreign Studies, Pusan (Young Jung Yu)
Abstract	Currently the traffic information services of Telematics have had high qualities due to easy collection of the real-time traffic information through intelligent transport system (ITS). In this work, a short-term prediction model is proposed for forecasting the traffic information. The Bayesian network is used for each link with some casual nodes which can affect road situations in the future. In addition, a joint probability density function of the Bayesian network is obtained by assuming Gaussian Mixture Model (GMM) which utilizes training data set. To validate the precision of our model we conducted various experiments with two measures, one is an index as root mean square error (RMSE) and the other is travel time which takes three kinds of shortest paths for given paths. Our model provides less than 8 value of RMSE and the travel time of dynamic shortest path has more than 85% correlation with the real traffic data.
Starting Page	242
Ending Page	247
File Size	525155
Page Count	6
File Format	PDF
ISBN	9780769534077
DOI	10.1109/ICCIT.2008.355
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-11-11
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Real time systems Roads Telecommunication traffic Predictive models ITS Short-term prediction Bayesian methods Traffic Information Forecasting Training data Traffic control Probability density function Telematics Intelligent systems
Content Type	Text
Resource Type	Article

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