Overall performance impairment and crash risk due to distracted driving: A comprehensive analysis using structural equation modelling |
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| Institution: | 1. Transportation Engineering Group, Department of Civil Engineering, Indian Institute of Technology (IIT) Roorkee, Roorkee 247667, Uttarakhand, India;2. Transportation Systems Engineering, Department of Civil Engineering, Indian Institute of Technology (IIT) Bombay, Powai, Mumbai 400076, India;3. Transportation Engineering, Department of Civil Engineering, Indian Institute of Technology (IIT) Hyderabad, Hyderabad 502285, India;1. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China;2. Hampton Hall of Civil Engineering G167B, Purdue University, West Lafayette, IN 47907, United States;3. Transportation Research Specialist, Institute for Transportation, Iowa State University, Ames, IA 50010, United States;1. Centre for Accident Research and Road Safety-Queensland (CARRS-Q), Institute of Health and Biomedical Innovation (IHBI), Queensland University of Technology (QUT), Kelvin Grove, 4059, Australia;2. Department of Industrial Engineering, Universidad del Norte, Barranquilla, Colombia;3. RECOVER Injury Research Centre, Faculty of Health and Behavioural Sciences, University of Queensland, Herston, Brisbane 4006, Australia;1. William Jewell College, United States;2. Old Dominion University, United States;1. Beijing Engineering Research Center of Urban Transport Operation Guarantee, College of Metropolitan Transportation, Beijing University of Technology, China;2. Texas Department of Transportation, United States |
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| Abstract: | Distracted driving leads to performance changes in both longitudinal and lateral control. However, driving performance, being a multidimensional phenomenon, is very difficult to be interpreted from individual performance measures. The present study aimed to exhibit the distraction impacts on overall driving performance estimated by a single measure rather than assessing distraction effects on individual performance metrics such as speed, acceleration, lateral variation, etc. The study also focused on modelling and quantifying the impacts of overall deteriorated driving performance on crash risk. To achieve the objectives, a comparative analysis of investigating phone and music player usage was conducted using Structural Equation Modelling (SEM). In total, 90 drivers’ demographic details and driving performance data in distracted and non-distracted driving environments were collected. Firstly, the latent variable “performance degradation” was derived from longitudinal and lateral performance measures. Then the structural model revealed a positive relationship between the distractions and the overall performance degradation. Finally, the crash risk was modelled against the presence of distraction and performance degradation. With a factor loading of 0.29, the impact of deteriorated driving performance (i.e., indirect impact) was found to be the highest among all the contributory factors of the crash risk. Further, the results showed that among the distractions (i.e., direct impact), texting had the highest impact (factor loading = 0.28) on crash risk followed by visual-manual tasks related to music player (factor loading = 0.21). Thus, the present study quantified the effects of deteriorated driving performance caused by distracted driving on the crash risk. Further, the study also presented quantified effects of each distracting activity on the crash risk which accounted for the factors that could not be considered through the performance degradation measure. The approach used in the present study can be adopted in designing the countermeasures using advanced driver warning systems. |
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| Keywords: | Crash risk Distracted driving Structural equation modelling Music player Performance degradation Phone use |
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