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Tracking and predicting a network traffic process.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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  • S. Garside
  • K. Lindveld
  • J. Whittaker
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<mark>Journal publication date</mark>1997
<mark>Journal</mark>International Journal of Forecasting
Volume13
Number of pages11
Pages (from-to)51-61
Publication StatusPublished
<mark>Original language</mark>English

Abstract

This article deals with the problem of real-time modelling and prediction of motorway traffic. Conditional independence relationships and ideas of Bayesian forecasting are proposed leading to the employment of dynamic state-space models, with optimal state estimation coming from the Kalman filter. Models, based on classical differential equations, which incorporate representations of the network topology are derived and are implemented in a state-space framework. The model is applied to several road networks in The Netherlands from which encouraging preliminary results are obtained.