Automated Road Segmentation Using a Bayesian Algorithm
Publication: Journal of Transportation Engineering
Volume 131, Issue 8
Abstract
Modern road profilers deliver long sequences of measurements on road characteristics including a road’s longitudinal and transversal unevenness. These measurements represent adjacent parts of the physical road, and interest focuses more on the overall pattern of these measurements than on each single value. In order to systematically assess the information contained in these measurement series, one typically wishes to partition a given series into segments, where each segment contains measurements which are “similar” to each other but “dissimilar” to the elements in the neighboring segments. An algorithm is suggested that combines a recently developed Bayesian identification of transitions between two homogeneous road sections with a heuristic approach that uses this technique iteratively to find multiple homogeneous sections in arbitrary long measurement series. The approach is demonstrated with narrowly spaced measurement series of the international roughness index as well as rutting.
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Acknowledgments
Part of this research was conducted under a contract with the Institute for Road Construction and Maintenance at Vienna University of Technology, Austria. The comments of two anonymous reviewers are gratefully acknowledged.
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Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 131 • Issue 8 • August 2005
Pages: 591 - 598
Copyright
© 2005 ASCE.
History
Received: Mar 16, 2004
Accepted: Sep 21, 2004
Published online: Aug 1, 2005
Published in print: Aug 2005
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