Using trajectory-level SHRP2 naturalistic driving data for investigating driver lane-keeping ability in fog: An association rules mining approach.

Das, Anik; Ahmed, Mohamed M; Ghasemzadeh, Ali

Das, Anik; Ahmed, Mohamed M; Ghasemzadeh, Ali.

Affiliation

Das A; University of Wyoming, Department of Civil & Architectural Engineering, 1000 E University Ave, Dept. 3295, Laramie, WY 82071, United States. Electronic address: adas4@uwyo.edu.
Ahmed MM; University of Wyoming, Department of Civil & Architectural Engineering, 1000 E University Ave, Dept. 3295, Laramie, WY 82071, United States. Electronic address: mahmed@uwyo.edu.
Ghasemzadeh A; University of Wyoming, Department of Civil & Architectural Engineering, 1000 E University Ave, Dept. 3295, Laramie, WY 82071, United States. Electronic address: aghasemz@uwyo.edu.

Accid Anal Prev ; 129: 250-262, 2019 Aug.

Article in En | MEDLINE | ID: mdl-31176145

Subject(s)

Automobile Driving/psychology; Weather; Accidents, Traffic/prevention & control; Adult; Automobile Driving/statistics & numerical data; Case-Control Studies; Female; Humans; Male; Middle Aged; Sex Distribution; Young Adult

Key words

Association rules mining; Data mining techniques; Foggy weather conditions; Lane-keeping; Limited visibility; Naturalistic driving study; SHRP2

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Automobile Driving / Weather Type of study: Observational_studies / Risk_factors_studies Limits: Adult / Female / Humans / Male / Middle aged Language: En Journal: Accid Anal Prev Year: 2019 Document type: Article Country of publication: United kingdom

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