{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T18:47:01Z","timestamp":1787165221862,"version":"3.56.0"},"reference-count":18,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T00:00:00Z","timestamp":1719878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korea Institute of Police Technology (KIPoT)","award":["092021C26S02000"],"award-info":[{"award-number":["092021C26S02000"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate detection of road surface conditions in adverse winter weather is essential for traffic safety. To promote safe driving and efficient road management, this study presents an accurate and generalizable data-driven learning model for the estimation of road surface conditions. The machine model was a support vector machine (SVM), which has been successfully applied in diverse fields, and kernel functions (linear, Gaussian, second-order polynomial) with a soft margin classification technique were also adopted. Two learner designs (one-vs-one, one-vs-all) extended their application to multi-class classification. In addition to this non-probabilistic classifier, this study calculated the posterior probability of belonging to each group by applying the sigmoid function to the classification scores obtained by the trained SVM. The results indicate that the classification errors of all the classifiers, excluding the one-vs-all linear learners, were below 3%, thereby accurately classifying road surface conditions, and that the generalization performance of all the one-vs-one learners was within an error rate of 4%. The results also showed that the posterior probabilities can analyze certain atmospheric and road surface conditions that correspond to a high probability of hazardous road surface conditions. Therefore, this study demonstrates the potential of data-driven learning models in classifying road surface conditions accurately.<\/jats:p>","DOI":"10.3390\/s24134307","type":"journal-article","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T09:01:39Z","timestamp":1719910899000},"page":"4307","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Using Support Vector Machines to Classify Road Surface Conditions to Promote Safe Driving"],"prefix":"10.3390","volume":"24","author":[{"given":"Jaepil","family":"Moon","sequence":"first","affiliation":[{"name":"Department of Highway & Transportation Research, Korea Institute of Civil Engineering and Building Technology, Goyang 10223, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wonil","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Highway & Transportation Research, Korea Institute of Civil Engineering and Building Technology, Goyang 10223, Republic of Korea"},{"name":"Department of Civil & Environmental Engineering, Seoul National University, Seoul 08826, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,2]]},"reference":[{"key":"ref_1","unstructured":"(2023, April 11). Samsung Fire & Marine Insurance. Available online: https:\/\/sts.samsungfire.com."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1109\/MNET.002.2200433","article-title":"Index modulation multiple access for 6G communications: Principles, applications, and challenges","volume":"37","author":"Li","year":"2023","journal-title":"IEEE Netw."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1949","DOI":"10.1109\/JSAC.2019.2929453","article-title":"A survey on spatial modulation in emerging wireless systems: Research progresses and applications","volume":"37","author":"Wen","year":"2019","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Abdic, I., Fridman, L., Marchi, E., Brown, D.E., Angell, W., Reimer, B., and Shculler, B. (2015). 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