Abstract Detecting aggressive driving behavior is essential for safe transport systems as it leads to the awareness of risks of accidents. Using smartphone-equipped sensors would be promising approach considering the penetration ratio to the consumers. In this paper, we have used a large dataset of accelerometer readings collected by smartphones of drivers. The experiment was performed to explore the accident risk indexes which statistically separate the safe drivers and risky drivers. By the statistical analysis, it is found that the frequency of acceleration exceeding 2.4 m/s2, that of deceleration exceeding 1.4 m/s2, and that of left acceleration exceeding 1.1 m/s2 separate the safe drivers and risky drivers. The classifier using these three criteria achieves 70 % classification accuracy and 83 % detection accuracy of risky drivers.
Analysis of Accident Risks from Driving Behaviors
International Journal of Intelligent Transportation Systems Research ; 15 , 3 ; 192-202
2016-09-01
11 pages
Article (Journal)
Electronic Resource
English
Driver behavior , Smartphone , Insurance telematics , Pay how you drive , Usage-based insurance Engineering , Electrical Engineering , Automotive Engineering , Robotics and Automation , Computer Imaging, Vision, Pattern Recognition and Graphics , Civil Engineering , User Interfaces and Human Computer Interaction
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