An approach to investigate the supplementary inconsistency between time series data for predicting road pavement performance models

Abstract

ABSTRACT Road agencies commonly face high level complexity of comprehensive pavement maintenance scheduling complications. This could be due to uncertainty of some parameters that substance in the predicting models. Disregarding this uncertainty of such parameters in the road maintenance scheduling problems may lead to imprecise solutions and unreliable road conditions. Frequently time series data that have a hierarchical structure are used for predicting road surface or pavement structure distresses models. However, further realistic and accurate predicted models are those consider supplementary inconsistency between time series data with utilising a predictive approach that imposes knowledge of the input parameters. The purpose of this study is to test this theory by comparing the predictions of models developed using two approaches and their accuracies in utilising the same data. These approaches are the traditional regression model (TRM) with only one level of heterogeneity approach and the hierarchical regression model (HRM) with many levels of heterogeneities approach. A Metro North Western (MNW) urban road network in Victoria (Australia) was considered as the case study for this study investigation where covering the condition of approximately 305 Km of asphalt surfaced pavements. By considering the structure of time series data, the study revealed that more accurate models could predict the pavement behaviour, the deterioration rate, and significantly influenced parameters. In addition, the study highlighted that it is essential to deliberate and process the hierarchical structure of datasets to consider the observed and unobserved heterogeneities in time series data for developing any pavement performance model using either deterministic or probabilistic prediction approaches. Multiple simulation scenarios have been performed for both approaches and the HRM approach shows more reasonable outputs. For predicted deterministic SIR models, road intervention (SIR ≥ 20) are triggered at age 12.5 when using TRM approach and at age 10.5 when using HRM approach. For predicted probabilistic SIR models, the surface condition of a section with SIR weighted average condition values of 2 is triggered at age 17.5 when using TRM approach and at age 10.5 when using HRM approach. This also refers to earlier maintenance scheduling is required when using the later approach because it deals confidentially with each road sections time series data. In that regard, ignoring the uncertainty in time series data of such factors in the road surfacing maintenance scheduling problems may lead to suboptimal solutions and unstable pavement conditions.