{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T14:24:52Z","timestamp":1783607092243,"version":"3.55.0"},"reference-count":40,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,4,19]],"date-time":"2020-04-19T00:00:00Z","timestamp":1587254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Driving risk varies substantially according to many factors related to the driven vehicle, environmental conditions, and drivers. This study explores the contributing historical factors of driving risk with hierarchical clustering analysis and the quasi-Poisson regression model. The dataset of the study was collected from two sources: naturalistic driving experiments and self-reports. The drivers who participated in the naturalistic driving experiment were categorized into four risk groups according to their near-crash frequency with the hierarchical clustering method. Moreover, a quasi-Poisson model was used to identify the essential factors of individual driving risk. The findings of this study indicated that historical driving factors have substantial impacts on individual risk of drivers. These factors include the total number of miles driven, the driver\u2019s age, the number of illegal parking (past three years), the number of over-speeding (past three years) and passing red lights (past three years). The outcome of the study can help transportation officials, educators, and researchers to consider the influencing factors on individual driving risk and can give insights and provide suggestions to improve driving safety.<\/jats:p>","DOI":"10.3390\/s20082331","type":"journal-article","created":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T04:49:38Z","timestamp":1587444578000},"page":"2331","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Investigating the Significant Individual Historical Factors of Driving Risk Using Hierarchical Clustering Analysis and Quasi-Poisson Regression Model"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2035-0759","authenticated-orcid":false,"given":"Hasan A.H.","family":"Naji","sequence":"first","affiliation":[{"name":"School of Computer and Information Engineering, Nanyang Institute of Technology, Chang Jiang Road No 80, Nanyang 473004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingji","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Nanyang Institute of Technology, Chang Jiang Road No 80, Nanyang 473004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Nanyang Institute of Technology, Chang Jiang Road No 80, Nanyang 473004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0926-9140","authenticated-orcid":false,"given":"Nengchao","family":"Lyu","sequence":"additional","affiliation":[{"name":"Intelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan 430063, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,19]]},"reference":[{"key":"ref_1","unstructured":"Ministry of Public Security Traffic Management Bureau (2019). 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