## 6 Conclusions  
This study is the first of various steps to predict the future pavement condition index based on machine learning using the PMS database, and it can be considered pioneering work. This study, in particular, used the CPCI to establish a process for predicting the future pavement condition. The conclusions of this study are as follows. 1. This study applied particle filtering on time-series data of the concrete pavement condition of a specific section, which were collected for research, to predict pavement conditions. As a result of reviewing 22 different cases, the particle filtering technique showed a prediction accuracy between 86.09 and 99.33%, indicating its applicability in predicting the road pavement condition index. As national investigations are conducted for both express highways and national highways every year, unlike in the past, the utilization of PMS can be further increased for project level management, where it was difficult to use in the past, through more accurate predictions. 2. The results of this study can be more easily utilized by the road pavement maintenance management team for making decisions in the future by presenting the deterioration model in the form of a category according to the performance index obtained at the time of the actual completion of the new section. In particular, modeling through future research is expected to allow practitioners to easily use it without further concerns by presenting the y-intercept value, CPCI at the time of completion, in a graph format for a more subdivided performance index, in addition to 4.2, 4.4, and 4.6. 3. The function of the particle filtering technique itself increases the prediction accuracy for the analysis target section as the number of particles increases; however, the accuracy for other sections (routes) can be lowered by specifying the corresponding section. Therefore, as a result of analyzing the accuracy of cases using 1000, 5000, 10,000, 15,000, and 20,000 particles, the use of 15,000 particles was found to be the most efficient. 4. We found that the number of time series points in one section had a higher impact on the improvement of prediction accuracy than the number of sections analyzed. The prediction accuracy was 92.3% when using the section with 5 points (261), whereas it was 98.1% when using the section with 7 points (208) (when 15,000 particles were used). 5. As a result of analyzing the prediction accuracy by increasing the number of points to 3, 4, 5, and 6 in the section with 7 points, the prediction accuracy was 88.5%, 92.9%,  
Figure 6: Results of prediction accuracy according to the change in the number of time-series points in the same section. (**a**) Influence of the number of actual data used for updating on the prediction of CPCI by the particle filtering; the lines are the mean values; (**b**) relative errors according to amount of data used for updating. 93.2%, and 99.3%, respectively. Accordingly, we determined that a section with at least six consecutive data points should be selected to secure a prediction accuracy of 95% or more in a study related to the prediction of the road pavement condition index, thereby obtaining the reliability of data and results. * The relative error by each predicted age for the same section decreased as the number of time series points increased. Meanwhile, prediction accuracy decreased when farther future was predicted in the section. Accordingly, the particle filtering technique is deemed to be effective to predict up to two years of future road pavement conditions. Conceptualization, D.-H.J. ; methodology, D.-H.J. and J.-H.L. ; software, J.-H.L. ; validation, D.-H.J. and J.-H.L. ; formal analysis, J.-H.L. ; investigation, D.-H.J. ; resources, M.-S.L. and S.-I.J. ; data curation, D.-H.J. ; writing--original draft preparation, D.-H.J. and J.-H.L. ; writing--review and editing, D.-H.J., M.-S.L., and S.-I.J. ; visualization, [PERSON]-H.L. ; supervision, [PERSON]-H.J. ; project administration, [PERSON]-H.J. ; funding acquisition, M.-S.L.