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Tracking and predicting a network traffic process.

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Tracking and predicting a network traffic process. / Garside, S.; Lindveld, K.; Whittaker, J.
In: International Journal of Forecasting, Vol. 13, 1997, p. 51-61.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Garside, S, Lindveld, K & Whittaker, J 1997, 'Tracking and predicting a network traffic process.', International Journal of Forecasting, vol. 13, pp. 51-61. https://doi.org/10.1016/S0169-2070(96)00700-5

APA

Garside, S., Lindveld, K., & Whittaker, J. (1997). Tracking and predicting a network traffic process. International Journal of Forecasting, 13, 51-61. https://doi.org/10.1016/S0169-2070(96)00700-5

Vancouver

Garside S, Lindveld K, Whittaker J. Tracking and predicting a network traffic process. International Journal of Forecasting. 1997;13:51-61. doi: 10.1016/S0169-2070(96)00700-5

Author

Garside, S. ; Lindveld, K. ; Whittaker, J. / Tracking and predicting a network traffic process. In: International Journal of Forecasting. 1997 ; Vol. 13. pp. 51-61.

Bibtex

@article{0ce2503f7dde430f817af617bb65537c,
title = "Tracking and predicting a network traffic process.",
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.",
keywords = "Motorway networks, Traffic dynamics, State-space model, Kalman filter, Independence graph",
author = "S. Garside and K. Lindveld and J. Whittaker",
year = "1997",
doi = "10.1016/S0169-2070(96)00700-5",
language = "English",
volume = "13",
pages = "51--61",
journal = "International Journal of Forecasting",
publisher = "Elsevier Science B.V.",

}

RIS

TY - JOUR

T1 - Tracking and predicting a network traffic process.

AU - Garside, S.

AU - Lindveld, K.

AU - Whittaker, J.

PY - 1997

Y1 - 1997

N2 - 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.

AB - 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.

KW - Motorway networks

KW - Traffic dynamics

KW - State-space model

KW - Kalman filter

KW - Independence graph

U2 - 10.1016/S0169-2070(96)00700-5

DO - 10.1016/S0169-2070(96)00700-5

M3 - Journal article

VL - 13

SP - 51

EP - 61

JO - International Journal of Forecasting

JF - International Journal of Forecasting

ER -