NDLI: Short-term traffic flow forecasting using Sampling Markov Chain method with incomplete data

Content Provider	IEEE Xplore Digital Library
Author	Shiliang Sun Guoqiang Yu Changshui Zhang
Copyright Year	2004
Description	Author affiliation: Dept. of Autom., Tsinghua Univ., Beijing, China (Shiliang Sun; Guoqiang Yu; Changshui Zhang)
Abstract	Short-term traffic flow forecasting is an important problem in the research area of intelligent transportation system. In practical situations, flow data may be incomplete, that is, partially missing or unavailable, where few methods could implement forecasting successfully. A method called Sampling Markov Chain is proposed to deal with this circumstance. In this paper, the traffic flow is modeled as a high order Markov Chain; and the transition probability from one state to the other state is approximated by Gaussian Mixture Model (GMM) whose parameters are estimated with Competitive Expectation Maximum (CEM) algorithm. The incomplete data in forecasting the trend of Markov Chain is represented by enough points sampled using the idea of Monte Carlo integration. Experimental results show that the Sampling Markov Chain method is applicable and effective for short-term traffic flow forecasting in case of incomplete data.
Starting Page	437
Ending Page	441
File Size	451154
Page Count	5
File Format	PDF
ISBN	0780383109
DOI	10.1109/IVS.2004.1336423
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-14
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sampling methods Traffic control Intelligent transportation systems Predictive models Monte Carlo methods Parameter estimation State estimation Sun Prediction methods Neural networks
Content Type	Text
Resource Type	Article

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