Spectral Basis Neural Networks for Real-Time Travel Time Forecasting
Publication: Journal of Transportation Engineering
Volume 125, Issue 6
Abstract
This paper examines how real-time information gathered as part of intelligent transportation systems can be used to predict link travel times for one through five time periods ahead (of 5-min duration). The study employed a spectral basis artificial neural network (SNN) that utilizes a sinusoidal transformation technique to increase the linear separability of the input features. Link travel times from Houston that had been collected as part of the automatic vehicle identification system of the TranStar system were used as a test bed. It was found that the SNN outperformed a conventional artificial neural network and gave similar results to that of modular neural networks. However, the SNN requires significantly less effort on the part of the modeler than modular neural networks. The results of the best SNN were compared with conventional link travel time prediction techniques including a Kalman filtering model, exponential smoothing model, historical profile, and real-time profile. It was found that the SNN gave the best overall results.
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Information & Authors
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Published In
Journal of Transportation Engineering
Volume 125 • Issue 6 • November 1999
Pages: 515 - 523
History
Received: Oct 5, 1998
Published online: Nov 1, 1999
Published in print: Nov 1999
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