A methodology for probabilistic pavement condition forecast based on Bayesian filters

Abstract

Decision making in pavement management relies on current road condition and the condition forecast. In this study, it is shown that both the condition forecast as well as the condition measurements are affected by uncertainties as demonstrated in a literature review and in preliminary studies of road condition data. If these uncertainties are to be considered in forecast models, the need for a probabilistic approach is evident. In this study a methodology based on an Extended Kalman filter (EKF) was developed and tested, which allows combining both empirical models and collected condition data for the development of section-based pavement forecast models. The model has been validated to predict the condition state effectively for all selected condition indicators. All relevant steps for the condition forecast have been implemented into a prototype to evaluate the applicability of the methodology using collected data on road networks from Germany, Austria, and Switzerland.

Keywords:

• Road condition data
• pavement management
• probabilistic forecast
• extended Kalman filter
• case study
• performance models
• condition forecasting

Acknowledgements

This work was funded by the German Federal Ministry of Transport and Digital Infrastructure (BMVI), the Austrian Federal Ministry of Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK), and the Swiss Federal Roads Office (FEDRO). This work was supported by the Austrian Research Promotion Agency (FFG) in cooperation with delegations from the German Federal Highway Research Institute (BASt), BMK, and FEDRO. The authors would like to thank BASt, the Austrian Autobahnen- und Schnellstraßen-Finanzierungs-Aktiengesellschaft (ASFINAG), and FEDRO for data provision, user insight and valuable comments in support of this study. The authors would also like to thank Prof. Dr.-Ing. J. Stefan Bald of the Institute for Road and Pavement Engineering at the Technical University of Darmstadt for his support in regard to the conception of the paper.

Disclosure statement

No potential conflict of interest was reported by the authors.