A methodology for probabilistic pavement condition forecast based on Bayesian filters
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Date
2024
Publication Type
Journal Article
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yes
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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.
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Publication status
published
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Journal / series
Volume
20 (1)
Pages / Article No.
83 - 96
Publisher
Taylor & Francis
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Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Road condition data; pavement management; probabilistic forecast; extended Kalman filter; case study; performance models; condition forecasting
Organisational unit
03859 - Adey, Bryan T. / Adey, Bryan T.
02655 - Netzwerk Stadt u. Landschaft ARCH u BAUG / Network City and Landscape ARCH and BAUG