A generalised approach to outlier identification in pavement condition data

Abstract

Pavement condition data often provide the quantitative measure by which funding for maintenance is prioritised. It is vital that these data are of the best quality possible. Measurement data are prone to inaccuracies and will exhibit outliers and deviations from the inferred expected condition. If a systematic trend in outliers is identified, for example a range of outliers adjacent to each other, then there may be a systematic bias in the measurements which can be reduced or removed from future data measurements. This paper introduces a new approach of visualising the probability of measurement outliers by representing the network as a distance/time matrix, with colours associated with different outlier probabilities. To assist in identifying significant outlier trends, a combined method of supervised data mining is proposed, combining expert knowledge and a new minimum message length criterion to select significant trends of systematic outliers.

Keywords:

• pavement
• condition
• outliers
• data mining
• MML
• visualisation