Predicting Urban Arterial Travel Time with State-Space Neural Networks and Kalman Filters

A hybrid model for predicting urban arterial travel time on the basis of so-called state-space neural networks (SSNNs) and the extended Kalman filter (EKF) is presented. Previous research demonstrated that SSNNs can address complex nonlinear spatiotemporal problems. However, SSNN models require off-...

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Veröffentlicht in:	Transportation research record 2006, Vol.1968 (1), p.99-108
Hauptverfasser:	Liu, Hao, Van Zuylen, Henk, Van Lint, Hans, Salomons, Maria
Format:	Artikel
Sprache:	eng
Online-Zugang:	Volltext
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