Tracking and predicting a network traffic process

Joe Whittaker

doi:10.1016/S0169-2070(96)00700-5

Tracking and predicting a network traffic process

1997-03-01

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DOI：10.1016/S0169-2070(96)00700-5

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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.

Keywords:

motorway networks

traffic dynamics

state-space model

Kalman filter

independence graph

Tracking and predicting a network traffic process

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