Automated road segmentation using a Bayesian algorithm

Research output: Contribution to journalArticle

14 Citations (Scopus)

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. Journal of Transportation Engineering

Original languageEnglish (US)
Pages (from-to)591-598
Number of pages8
JournalJournal of Transportation Engineering
Volume131
Issue number8
DOIs
StatePublished - Aug 1 2005

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road
segmentation
heuristics
Surface roughness
engineering
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All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Transportation

Cite this

Automated road segmentation using a Bayesian algorithm. / Thomas, Fridtjof.

In: Journal of Transportation Engineering, Vol. 131, No. 8, 01.08.2005, p. 591-598.

Research output: Contribution to journalArticle

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