Road transportation has always been inherent in developing societies, impacting between 10–20% of Gross Domestic Product (GDP). It is responsible for personal mobility (access to services, goods, and leisure), and that is why world economies rely upon the efficient and safe functioning of transportation facilities. Road maintenance is vital since the need for maintenance increases as road infrastructure ages and is based on sustainability, meaning that spending money now saves much more in the future. Furthermore, road maintenance plays a significant role in road safety. However, pavement management is a challenging task because available budgets are limited. Road agencies need to set programming plans for the short term and the long term to select and schedule maintenance and rehabilitation operations. Pavement performance prediction models (PPPMs) are a crucial element in pavement management systems (PMSs), providing the prediction of distresses and, therefore, allowing active and efficient management. This work aims to review the modeling techniques that are commonly used in the development of these models. The pavement deterioration process is stochastic by nature. It requires complex deterministic or probabilistic modeling techniques, which will be presented here, as well as the advantages and disadvantages of each of them. Finally, conclusions will be drawn, and some guidelines to support the development of PPPMs will be proposed.

This paper describes the characteristics of the new pavement design method developed by the American Association of State Highway and Transportation Officials (AASHTO), known as AASHTOWare Pavement-ME^®, and presents the results of its application to the flexible pavement structures presented in the Portuguese Manual of Pavement Structures for the national road network. The results obtained clearly show that it is a very useful tool for road engineers not only for designing new pavement structures but also for the analysis of their performance and for efficiently planning maintenance and rehabilitation interventions. According to the characteristics of the case study that was considered, rutting is the most critical distress, since it presents values close to its threshold value of 20.0 mm, a value that is defined in Portuguese Quality Control Plans.