Predicting drowsy driving in real-time situations: Using an advanced driving simulator, accelerated failure time model, and virtual location-based services

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• 作者：Junhua Wang^a ; ^{benwjh@163.com} ; Shuaiyi Sun^a ; ^{sshuaiyi@126.com} ; Shouen Fang^a ; ^{fangshouen@126.com} ; Ting Fu^b ; ^{ting.fu@mail.mcgill.ca} ; Joshua Stipancic^b ; ^{joshua.stipancic@mail.mcgill.ca}
• 关键词：Drowsy driving ; Duration prediction ; Accelerate failure time ; Location based service ; Drowsy driving warning system
• 刊名：Accident Analysis & Prevention
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：99
• 期：part_PA
• 页码：321-329
• 全文大小：1304 K
• 卷排序：99

文摘

Traditional drowsy driving warning systems use a fixed driving duration to alert drivers. Past studies have failed to fully explore the factors influencing drowsy driving time. This study investigates the impact of demographic and environmental factors on drowsy driving. The real-time prediction model included data collected using Location-based Services (LBS). The Accelerated Failure Time outperformed the General Log-Linear Model in goodness-of-fit.