Generalised clusterwise regression for simultaneous estimation of optimal pavement clusters and performance models

ABSTRACT

This paper focuses on clusterwise regression (CR) approach for modelling of pavement performance. CR simultaneously clusters the data and estimates the associated models. Previous studies using CR approach have a few limitations: (1) the explanatory power of variables used in the analyses was not tested; (2) the approach could not find the optimal number of clusters; (3) the objective function was to minimise the sum of squared errors, which is not the best to seek for the optimal number of clusters; (4) the model functional form was restricted to be either linear or nonlinear. To address these limitations, this paper proposes a generalised mathematical programme and solution algorithm within the CR framework. Bayesian Information Criteria was used as the objective function. The proposed approach explored all possible combinations of potential significant explanatory variables to select the best model specification. The potential multicollinearity issues in the models were addressed if required. Both linear and nonlinear functional forms were estimated using a large dataset in Nevada. Predictive accuracy of the resultant models was evaluated using root-mean-square error (RMSE), normalised RMSE, and mean absolute errors. The results showed that the nonlinear models were more accurate than the linear models in estimating present serviceability index.

KEYWORDS:

• Clusterwise regression
• Bayesian information criterion
• optimisation
• performance model
• pavement management

Acknowledgments

Many thanks to the Nevada Department of Transportation for providing the data that was used for analysis, and to our Technical Writer at UNLV’s Howard R. Hughes College of Engineering, Julie Longo, for her help reviewing this manuscript. Special thanks to Fabien Sortais for his help with the implementation of the solution algorithm. The authors would like to thank all the anonymous reviewers and editor for their constructive comments to improve the quality of paper.

Disclosure statement

No potential conflict of interest was reported by the authors.