Identification of aggressive driving from naturalistic data in car-following situations |
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| Institution: | 1. Chalmers University of Technology, Sweden;2. If P&C Insurance, Sweden;1. Driving Simulation Laboratory, The Ohio State University, United States;2. School of Communication, The Ohio State University, United States;1. Civil and Environmental Engineering Department, University of Tennessee, Knoxville, TN 37996, USA;2. Civil and Environmental Engineering, Department University of Maryland, College Park, MD 20742, USA;3. Jeju Development Institute, 53 Ayeon-ro, Jeju City 690-162, South Korea;1. Department of Civil, Structural and Environmental Engineering, Engineering Statistics and Econometrics Application Research Laboratory, University at Buffalo, The State University of New York, 204B Ketter Hall, Buffalo, NY, 14260, United States;2. Transport Research Institute, School of Engineering and the Built Environment, Edinburgh Napier University, 10 Colinton Road, Edinburgh, EH10 5DT, UK;3. Department of Civil, Structural and Environmental Engineering, Stephen Still Institute for Sustainable Transportation and Logistics, University at Buffalo, The State University of New York, 241 Ketter Hall, Buffalo, NY, 14260, United States;4. Public Safety & Transportation Group, CUBRC, 4455 Genesee St., Suite 106, Buffalo, NY, 14225, United States;1. University of Tennessee, Knoxville, TN 37996, USA;2. Civil and Environmental Engineering Department, University of Tennessee, Knoxville, TN 37996, USA;1. Department of Transportation Planning and Engineering, National Technical University of Athens, 5 Heroon Polytechniou Str., Athens GR-15773, Greece;2. OSeven Telematics Limited, 27B Chaimanta Str., Athens GR-15234, Greece |
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| Abstract: | Introduction: Aggressive driving has been associated as one of the causes for crashes, sometimes with very serious consequences. The objective of this study is to investigate the possibility of identifying aggressive driving in car-following situations on motorways by simple jerk metrics derived from naturalistic data. Method: We investigate two jerk metrics, one for large positive jerk and the other for large negative jerk, when drivers are operating the gas and brake pedal, respectively. Results: The results obtained from naturalistic data from five countries in Europe show that the drivers from different countries have a significantly different number of large positive and large negative jerks. Male drivers operate the vehicle with significantly larger number of negative jerks compared to female drivers. The validation of the jerk metrics in identifying aggressive driving is performed by tailgating (following a leading vehicle in a close proximity) and by a violator/non-violator categorization derived from self-reported questionnaires. Our study shows that the identification of aggressive driving could be reinforced by the number of large negative jerks, given that the drivers are tailgating, or by the number of large positive jerks, given that the drivers are categorized as violators. Practical applications: The possibility of understanding, classifying, and quantifying aggressive driving behavior and driving styles with higher risk for accidents can be used for the development of driver support and coaching programs that promote driver safety and are enabled by the vast collection of driving data from modern in-vehicle monitoring and smartphone technology. |
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| Keywords: | Aggressive driving Jerk metrics Naturalistic driving Car-following Self-reported questionnaires |
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