@JournalArticle {2463,
	title = {An application of Markov chains to predict the evolution of performance indicators based on pavement historical data},
	journal = {International Journal of Pavement Engineering},
	volume = {19},
	year = {2018},
	month = {2018-08-10 00:00:00},
	pages = {937 - 948},
	publisher = {Taylor \& Francis Ldt.},
	abstract = {
One of the main awareness for a road infrastructures manager is to increase its efficiency under limited resources. Pavement Management Systems aim, at last, to support road administrations in the decision-making process regarding its management policy and long-term strategies for maintenance and rehabilitation activities. While several road administrations are putting efforts in developing optimisation methodologies to enhance their decision making process, many still lack of data that allows the development of reliable prediction models for pavement performance. This is a key aspect to develop and test decision-making methodologies. Although there are several prediction models available in the literature, their practical applications are often limited to the very specific network from which data were retrieved at first and to a specific performance indicator (PI). This paper presents a practical application of a Markov model to predict the evolution of five PIs {\textendash} cracking, skid resistance, bearing capacity, longitudinal evenness and transverse evenness {\textendash} and consequent combined PIs, using historical data from an extensive pavement database. The conversion for PIs is made through a standardisation procedure proposed by an European COST Action, whichmay be considered a reference classification system for road administrations. The presented model is intended to be an useful input for researchers and administrations willing to develop and test different optimisation approaches.
},
	keywords = {global performance indicators, Markov chains, pavement deterioration, Prediction models, Road infrastructure management},
	issn = {1029-8436 (Print); 1477-268X (Online)},
	doi = {10.1080/10298436.2016.1224412},
	url = {http://dx.doi.org/10.1080/10298436.2016.1224412},
	author = {Moreira, A. V. and Tinoco, J. and Oliveira, J. R. M. and Santos, A.}
}