Reliable Real-Time Framework for Short-Term Freeway Travel Time Prediction
Publication: Journal of Transportation Engineering
Volume 132, Issue 12
Abstract
It is widely acknowledged that traffic information has the potential of increasing the reliability in road networks and in alleviating congestion and its negative environmental and societal side effects. However, for these beneficial collective effects to occur, reliable and accurate traffic information is a prerequisite. Building on previous research, this article presents a reliable framework for online travel time prediction for freeways, which could, for example, be used to generate traffic information messages on so-called dynamic route information panels on freeways. Central in this framework is a so-called state-space neural network (SSNN) model, which learns to predict travel times directly from data obtained from real time traffic data collection systems. In this article we show that by using an ensemble of SSNN models also a measure for the reliability of each prediction can be produced. This enables traffic managers to monitor in real time the reliability of this system without actually measuring travel times.
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Acknowledgments
The writer is indebted to the anonymous reviewers whose detailed comments and remarks were very helpful in improving and clarifying this paper. This research is part of the Regiolab-Delft program (Van Zuylen and Muller 2002), the Advanced Traffic Monitoring (ATMO) project (Van Lint 2004a) within the Transumo (www.transumo.nl) research program, and was partially sponsored by the Dutch Ministry of Transport, Public Works and Water Management.
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Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 132 • Issue 12 • December 2006
Pages: 921 - 932
Copyright
© 2006 ASCE.
History
Received: Feb 25, 2005
Accepted: May 31, 2006
Published online: Dec 1, 2006
Published in print: Dec 2006
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