## 5 Conclusions  
Pavement performance significantly changes road user benefits during the pavement life cycle. This makes value-based decision-making models highly sensitive to the prediction of pavement performance. The use of empirical pavement models generalized for a particular group or type of pavement does not allow for meaningful socio-economic value-based comparison and can produce inaccurate false results for the prioritization or optimization of pavement rehabilitation or road network development. Mechanistic-empirical pavement performance models combine measured or calculated pavement responses to stress and strains and, combined with deformational properties and fatigue of the asphalt mixtures, can predict pavement failure due to the loss of bearing capacity and the accelerated pavement deterioration in the later stages of pavement life.  
The application of empirical models generally faces applicability issues. Several assumptions and simplifications are necessary for the application of the presented model:  
1. Traffic intensity, composition, and growth are based on traffic data collection and prediction; no probability models are used to account for unexpected deviations.  
2. Pavements in operation may differ from APT pavement sections and LTPPM pavement sections; this model is used for flexible pavements with a life expectancy of 3.5 mil SAL. These pavements constitute an overwhelming majority of pavements in secondary and tertial road networks that are the intended targets of this model.  
3. The model presumes that the pavement structure was always constructed following technical standards; it does not account for faults in constructions due to non-compliance with construction regulations.  
4. The model accounts for slight significant variations in climatic conditions; this reflects the majority of conditions in the country where it is used. While it is used also for pavements in mountainous regions and southern lowlands, the results that it provides may carry slight errors due to the different climatic conditions in these regions.  
This article presents the summarized methodology and selected input data acquisition for the creation of a mechanistic-empirical pavement performance model and highlights the impact on the reliability of socio-economic value-based decision-making. The methodology may seem laborious, but much of it can be automated by the use of appropriate software solutions, and the potential costs of its implementation are outweighed by a PMS that produces credible results. Considering the magnitude of rehabilitation works performed every year, all costs incurred in the implementation of this method or similar methods are negligible compared to the benefits from an optimized road network rehabilitation program.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14093880/s1](https://www.mdpi.com/article/10.3390/app14093880/s1)]([https://www.mdpi.com/article/10.3390/app14093880/s1](https://www.mdpi.com/article/10.3390/app14093880/s1)), Raw data for analysis and support data for Figures 4, 9, 11, 13 and 14.  
Conceptualization, M.K. and L.R.; methodology, M.K.; software, L.R.; validation, L.R., M.K. and K.I.; formal analysis, G.M. and P.B.; investigation, M.K.; resources, K.I.; data curation, L.R.; writing--original draft preparation, M.K.; writing--review and editing, L.R.; visualization, M.K.; supervision, G.M. and P.B.; project administration, K.I.; funding acquisition, M.K. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the Slovak Research and Development Agency under contract no. APVV-22-0040.  
Not applicable.  
The data are contained within the article or Supplementary Materials.  
The data for the LTPPM model were provided by the Slovak Road Administration agency.  
The authors declare no conflicts of interest.