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After identifying strong relationships between the slip and the acceleration data on dry surfaces, three approaches, including regression analysis, SVM, and deep learning models, were applied to categorize the pavement states slippery or non slippery. As a result, SVM models resulted in reasonably good performances with an average accuracy of 96. 5%, although the deep learning model showed the best performance for one of the three analysis groups that has the largest sample size. A follow up experiment is scheduled in wintertime in 2021 to further investigate that the approach of this study can be applied for data from trucks and buses that carry several kinds of LBS devices for fleet management. In addition, deep learning algorithms need to be further investigated with more data in groups 2 and 3 in Table 2 to find out if the performance of group 1 can be applied to other groups with sufficient data. Lastly, the effect of the road slope for determining thresholds for classifying pavement states needs to be examined. The data that support the findings of this study are available from the corresponding author J. J upon reasonable request. This work was supported by a grant No. 19TLRP B148886 02 from the Korea Agency for Infrastructure Technology Advancement KAIA. Prof.

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