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This paper describes the development of a stochastic model for the strategic assessment of road maintenance funding and policy decisions. The algorithms used for simulating pavement deterioration utilise Markov processes, resulting in a transition probability matrix that defines defect progression as opposed to the more familiar regression-type model popular with engineers. The model is designed to be used by road maintenance managers and senior administrators for planning medium to long term maintenance investment requirements for local and national road networks. Finally, the application of the model is demonstrated with a case study from central Europe. Background This paper describes the results of research carried out at the University of Birmingham over a period of some 10 years in the development of a stochastic model for the strategic assessment of road maintenance funding and policy decisions.

This paper discusses the evolution of highway maintenance standards determination, from purely engineering judgement through the application of scientific research to the inclusion of economic assessment in the process. It then proceeds to discuss more recent initiatives that have identified the need to include not only engineering and economics in the determination of maintenance standards but also social and political factors, together with environmental aspects where appropriate. Finally, multicriteria analysis (MCA) is suggested as an appropriate decision-making tool capable of taking proper account of the dominant social, political and environmental issues in an all-embracing and logical process. The suitability of four candidate MCA methodologies was studied: the utility theory approach, the goals programming approach, methods based on the outranking concept and the analytic hierarchy process (AHP). The research has concluded that the AHP is the most appropriate to use, at least initially, owing to its operational advantages. An example of its use in the determination of highway maintenance standards is provided.