{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T18:29:29Z","timestamp":1779906569908,"version":"3.53.1"},"reference-count":33,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T00:00:00Z","timestamp":1648684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>The problem of reducing the increasing number of road traffic accidents has become more relevant in recent years. According to the United Nations plan this number has to be halved by 2030. A very effective way to handle it is to apply the machine learning paradigm to retrospective road traffic accident datasets. This case study applies machine learning techniques to form typical \u201cportraits\u201d of drivers violating road traffic rules by clustering available data into seven homogeneous groups. The obtained results can be used in forming effective marketing campaigns for different target groups. Another relevant problem under consideration is to use available retrospective statistics on mechanical road traffic accidents without victims to estimate the probable type of road traffic accident for the driver taking into account her\/his personal features (such as social characteristics, typical road traffic rule violations, driving experience, and age group). For this purpose several machine learning models were applied and the results were discussed.<\/jats:p>","DOI":"10.3390\/computation10040057","type":"journal-article","created":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T21:33:03Z","timestamp":1648762383000},"page":"57","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Applying Machine Learning Methods and Models to Explore the Structure of Traffic Accident Data"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0866-1124","authenticated-orcid":false,"given":"Anton","family":"Sysoev","sequence":"first","affiliation":[{"name":"Department of Applied Mathematics, Lipetsk State Technical University, Moskovskaya Str., 30, RU-398055 Lipetsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7839-4463","authenticated-orcid":false,"given":"Vladimir","family":"Klyavin","sequence":"additional","affiliation":[{"name":"Department of Traffic Control, Lipetsk State Technical University, Moskovskaya Str., 30, RU-398055 Lipetsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandra","family":"Dvurechenskaya","sequence":"additional","affiliation":[{"name":"State Traffic Safety Inspectorate of Lipetsk Region, Nedelina Str., 2, RU-398059 Lipetsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Albert","family":"Mamedov","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics, Lipetsk State Technical University, Moskovskaya Str., 30, RU-398055 Lipetsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vladislav","family":"Shushunov","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics, Lipetsk State Technical University, Moskovskaya Str., 30, RU-398055 Lipetsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,31]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2022, February 10). 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