Drowsiness and distraction while driving: A study based on smartphone app data |
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| Institution: | 1. The Center for Injury Research and Policy, Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, OH, United States;2. Department of Pediatrics, College of Medicine, The Ohio State University, Columbus, United States;3. Graduate Center for Gerontology, College of Public Health, University of Kentucky, Lexington, KY, United States;4. Department of Civil Engineering, College of Engineering, University of Kentucky, Lexington, KY, United States;5. Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, OH, United States;1. Research Centre for Territory, Transports and Environment (CITTA), Faculty of Engineering, University of Porto, Portugal;2. Vias Institute Brussels, Belgium;3. UHasselt- Hasselt University, Transportation Research Institute (IMOB), Belgium;1. Human Factors and Safety Behavior Group, Institute of Behavioral Sciences, University of Helsinki, Helsinki, Finland;2. Stress Research Institute, Stockholm University, Sweden;3. Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden;4. Centre for Accident Research and Road Safety – Queensland, Institute of Health and Biomedical Innovation, Queensland University of Technology, Australia;5. Behavioural Science Institute, Radboud University Nijmegen, The Netherlands;1. Transportation Systems Engineering, Department of Civil Engineering, Indian Institute of Technology (IIT) Bombay, Powai, Mumbai 400 076, India;2. Industrial & Systems Engineering, Indian Institute of Technology (IIT) Kharagpur, Kharagpur 721302, India;3. School of Business and Economics, Loughborough University, Leicestershire LE11 3TU, UK |
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| Abstract: | Introduction: Due to the negative impact on road safety from driver drowsiness and distraction, several studies have been conducted, usually under driving simulator and naturalistic conditions. Nevertheless, emerging technologies offer the opportunity to explore novel data. The present study explores retrospective data, which was gathered by an app designed to monitor the driver, which is available to any driver owning a smartphone. Method: Drowsiness and distraction alerts emitted during the journey were aggregated by continuous driving (called sub-journey). The data include 273 drivers who made 634 sub-journeys. Two binary logit models were used separately to analyze the probability of a drowsiness and distraction event occurring. Variables describing the continuous driving time (sub-journey time), the journey time (a set of sub-journeys), the number of breaks, the breaking duration time and the first sub-journey (categorical variable) were included. Additionally, categorical variables representing the gender and age of the drivers were also incorporated. Results: Despite the limitations of the retrospective data, interesting findings were obtained. The results indicate that the main risk factor of inattention is driving continuously (i.e., without stopping), but it is irrelevant whether the stop is long or short as well as the total time spent on the journey. The probability of distraction events occurring during the journey is higher than drowsiness events. Yet, the impact of increasing the driving time of the journey and stopping during the journey on the probability of drowsiness is higher than the probability of distraction. Additionally, this study reveals that the elderly are more prone to drowsiness. The data also include a group of drivers, who did not provide information on gender and age, who were found to be associated to drowsiness and distraction risk. Conclusions: The study shows that data gathered by an app have the potential to contribute to investigating drowsiness and distraction. Practical applications: Drivers are highly recommended to frequently stop during the journey, even for a short period of time to prevent drowsiness and distraction. |
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| Keywords: | Drowsiness Distraction Alerts Smartphone app Risk |
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