A Bayesian network approach to time series forecasting of short-term traffic flows

A novel approach based on Bayesian networks for short-term traffic flow forecasting is proposed. A Bayesian network is originally used to model the causal relationship of time series of traffic flows among a chosen link and its adjacent links in a road network. Then, a Gaussian mixture model (GMM), whose parameters are estimated through competitive expectation maximization (CEM) algorithm, is applied to approximate the joint probability distribution of all nodes in the constructed Bayesian network. Finally, traffic flow forecasting of the current link is performed under the rule of minimum mean square error (MMSE). To further improve the forecasting performance, principal component analysis (PCA) is also adopted before carrying out the CEM algorithm. Experiments show that, by using a Bayesian network for short-term traffic flow forecasting, one can improve the forecasting accuracy significantly, and that the Bayesian network is an attractive forecasting method for such kinds of forecasting problems.

[1]  Neil Davey,et al.  Traffic trends analysis using neural networks , 1997 .

[2]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[3]  Daniel B. Fambro,et al.  Application of Subset Autoregressive Integrated Moving Average Model for Short-Term Freeway Traffic Volume Forecasting , 1999 .

[4]  Gary A. Davis,et al.  ADAPTIVE FORECASTING OF FREEWAY TRAFFIC CONGESTION , 1990 .

[5]  Edmund S. Yu,et al.  Traffic prediction using neural networks , 1993, Proceedings of GLOBECOM '93. IEEE Global Telecommunications Conference.

[6]  Jason Hall,et al.  The limitations of artificial neural networks for traffic prediction , 1998, Proceedings Third IEEE Symposium on Computers and Communications. ISCC'98. (Cat. No.98EX166).

[7]  Michael J Demetsky,et al.  TRAFFIC FLOW FORECASTING: COMPARISON OF MODELING APPROACHES , 1997 .

[8]  B. G. Ratcliffe,et al.  Short term traffic forecasting using time series methods , 1988 .

[9]  Gary A. Davis,et al.  Nonparametric Regression and Short‐Term Freeway Traffic Forecasting , 1991 .

[10]  Jeff A. Bilmes,et al.  A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models , 1998 .

[11]  Billy M. Williams,et al.  MODELING AND FORECASTING VEHICULAR TRAFFIC FLOW AS A SEASONAL STOCHASTIC TIME SERIES PROCESS , 1999 .

[12]  I Okutani,et al.  Dynamic prediction of traffic volume through Kalman Filtering , 1984 .

[13]  S. J. Kim,et al.  Traffic-flow forecasting using a 3-stage model , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[14]  Roland Chrobok,et al.  Traffic forecast using simulations of large scale networks , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).