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• W3040731021 abstract "Driver inattention is a major contributor to road crashes. The emerging of new driver monitoring systems represents an opportunity for researchers to explore new data sources to understand driver inattention, even if the technology was not developed with this purpose in mind. This study is based on retrospective data obtained from two driver monitoring systems to study distraction and drowsiness risk factors. The data includes information about the trips performed by 330 drivers and corresponding distraction and drowsiness alerts emitted by the systems. The drivers’ historical travel data allowed defining two groups with different mobility patterns (short-distance and long-distance drivers) through a cluster analysis. Then, the impacts of the driver’s profile and trip characteristics (e.g., driving time, average speed, and breaking time and frequency) on inattention were analyzed using ordered probit models. The results show that long-distance drivers, typically associated with professionals, are less prone to distraction and drowsiness than short-distance drivers. The driving time increases the probability of inattention, while the breaking frequency is more important to mitigate inattention than the breaking time. Higher average speeds increase the inattention risk, being associated with road facilities featuring a monotonous driving environment." @default.
• W3040731021 created "2020-07-16" @default.
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• W3040731021 date "2020-07-09" @default.
• W3040731021 modified "2023-10-16" @default.
• W3040731021 title "Exploring Monitoring Systems Data for Driver Distraction and Drowsiness Research" @default.
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• W3040731021 doi "https://doi.org/10.3390/s20143836" @default.
• W3040731021 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7412490" @default.
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