{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T18:55:12Z","timestamp":1774896912990,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,5,9]],"date-time":"2020-05-09T00:00:00Z","timestamp":1588982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spanish Ministry\/FEDER, Fundaci\u00f3n BBVA and National Natural Science Foundation of China","award":["71621001 and 71961137008"],"award-info":[{"award-number":["71621001 and 71961137008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the major advances made in internet of vehicles (IoV) technology in recent years, usage-based insurance (UBI) products have emerged to meet market needs. Such products, however, critically depend on driving risk identification and driver classification. Here, ordinary least square and binary logistic regressions are used to calculate a driving risk score on short-term IoV data without accidents and claims. Specifically, the regression results reveal a positive relationship between driving speed, braking times, revolutions per minute and the position of the accelerator pedal. Different classes of risk drivers can thus be identified. This study stresses both the importance and feasibility of using sensor data for driving risk analysis and discusses the implications for traffic safety and motor insurance.<\/jats:p>","DOI":"10.3390\/s20092712","type":"journal-article","created":{"date-parts":[[2020,5,11]],"date-time":"2020-05-11T12:26:30Z","timestamp":1589199990000},"page":"2712","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Assessing Driving Risk Using Internet of Vehicles Data: An Analysis Based on Generalized Linear Models"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5654-5104","authenticated-orcid":false,"given":"Shuai","family":"Sun","sequence":"first","affiliation":[{"name":"Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3838-0654","authenticated-orcid":false,"given":"Jun","family":"Bi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2644-6268","authenticated-orcid":false,"given":"Montserrat","family":"Guillen","sequence":"additional","affiliation":[{"name":"Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona, 08034 Barcelona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ana","family":"P\u00e9rez-Mar\u00edn","sequence":"additional","affiliation":[{"name":"Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona, 08034 Barcelona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hamid, U.Z.A., Zamzuri, H., and Limbu, D.K. 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