Matthew Byrne

A generalised approach to outlier identification in pavement condition data

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.

Meta-analytic framework for efficiently identifying progression groups in highway condition analysis

The minimum message length two-dimensional segmenter (MML2DS) criterion is a powerful technique for road condition data analysis developed at the Nottingham Transportation Engineering Centre (NTEC), University of Nottingham. The criterion analyses condition data sets by simultaneously identifying optimum trends in condition progression, the position in time and space of maintenance interventions, longitudinal segments within links, and the error likelihood of each measurement. This is done in an unsupervised manner through classification and regression models on the basis of the minimum message length (MML) metric. Use of MML, however, often requires an exhaustive comparison of all possible models, which naturally raises considerable search-control issues. This is precisely the case with the MML2DS approach. This paper presents an efficient meta-analytic framework for controlling the generation of progression groups, which considerably reduces the search space before the application of MML2DS. This is achieved by identifying founder sets of longitudinal segments, around which families of segments are likely to be formed. An effective subset of these families is then selected, after which the MML2DS criterion is used as the final arbiter to determine ultimate model configurations and fits. This approach has proved to be very powerful, resulting in significant improvements in efficiency to the effect that accurate results are obtained in a few minutes where it previously took weeks with much smaller data sets. The indications are that this approach can be applied to other techniques besides MML2DS.

Identifying Error and Maintenance Intervention of Pavement Roughness Time Series with Minimum Message Length Inference

Pavement roughness is a useful measure of pavement condition. One method of comparing alternative sections of pavements is the roughness progression rate (RPR). The objective of this paper is to describe difficulties in identifying RPR from real data and provide a new type of criteria to overcome these difficulties. Selecting appropriate regression functions for time-series roughness presents two major problems. Roughness time series can include roughness data that appear erroneous, acting independent of the observed time-series trend. Including likely error values will bias the calculated RPR. The problem of identifying likely error is made more difficult with the possibility of maintenance intervention, which may reduce the roughness level and/or progression rate. A minimum message length (MML) criterion to select RPR is introduced and is referred to herein as MML RPR. We perform simulated comparisons of common segmentation criterion and conclude that MML RPR is the preferred criterion.