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In particular, studying this issue in relation to young people aged 25 and below becomes more sensitive because they are not experienced and there are some age-related elements. Moreover, a significant percentage of beginner drivers fall into this age category, which can lead to risky behavior. Overconfidence, indiscipline, careless driving, or speeding tendencies may contribute greatly to their vulnerability to hazards on the roads. Hence, there is a need for further research to establish the constraints and possible risks involved with young drivers to improve road safety. Hence, this study aims to analyze the potential hazards associated with youth driving behavior in order to facilitate the development of relevant remedies through a thorough understanding of their behavior for safe transportation on the roads. To achieve this goal, a multi-criteria decision-making approach has been used. The proposed approach uses measurement of options and ranking based on the compromise solution method in an intuitive fuzzy environment to evaluate and rank risks. In addition, through consultation with experts and experienced technicians, a selection of 17 potential hazards were identified from existing risk factors. These risks are classified into three groups: working on the phone, distractions, and non-compliance. The present stud shows that risky driving and driving in reverse represent the highest level of risk, while speeding represents the lowest level of risk among young drivers.<\/jats:p>","DOI":"10.1007\/s00521-025-11087-8","type":"journal-article","created":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T19:41:12Z","timestamp":1741203672000},"page":"9565-9602","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Risk assessment of young driver behavior using an extended decision-making approach based on FMEA in uncertain environments"],"prefix":"10.1007","volume":"37","author":[{"given":"Saeid","family":"Jafarzadeh Ghoushchi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sami","family":"Shaffiee Haghshenas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sahand","family":"Vahabzadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2859-3920","authenticated-orcid":false,"given":"Sina","family":"Shaffiee Haghshenas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vittorio","family":"Astarita","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giuseppe","family":"Guido","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,5]]},"reference":[{"key":"11087_CR1","first-page":"100164","volume":"12","author":"V Astarita","year":"2023","unstructured":"Astarita V, Haghshenas SS, Guido G, Vitale A (2023) Developing new hybrid grey wolf optimization-based artificial neural network for predicting road crash severity. 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