Abstract
This study proposes a method for identifying and evaluating driving risk as a first step towards calculating premiums in the newly emerging context of usage-based insurance. Telematics data gathered by the Internet of Vehicles (IoV) contain a large number of near-miss events which can be regarded as an alternative for modeling claims or accidents for estimating a driving risk score for a particular vehicle and its driver. Poisson regression and negative binomial regression are applied to a summary data set of 182 vehicles with one record per vehicle and to a panel data set of daily vehicle data containing four near-miss events, i.e., counts of excess speed, high speed brake, harsh acceleration or deceleration and additional driving behavior parameters that do not result in accidents. Negative binomial regression ( = 997.0, = 1022.7) is seen to perform better than Poisson regression ( = 7051.8, = 7074.3). Vehicles are separately classified to five driving risk levels with a driving risk score computed from individual effects of the corresponding panel model. This study provides a research basis for actuarial insurance premium calculations, even if no accident information is available, and enables a precise supervision of dangerous driving behaviors based on driving risk scores.
Highlights
Near-miss events are incidents that denote the existence of danger, even if no accident occurs
Accelerator Pedal Positon brakes range speed rpm accelerator pedal positon engine fuel rate. Both Poisson regression and negative binomial regression are applicable to this study, and the zero-inflated model is taken as a consideration for the large number of zero values of dependent variables
In the summary data set, four near-miss events are respectively treated as dependent variables while the independent variables are brakes, speed, rpm, accelerator pedal position and engine fuel rate, where kilo is chosen as the exposure variable or offset
Summary
Near-miss events are incidents that denote the existence of danger, even if no accident occurs. Reporting of near-miss events is an established error reduction technique that has been used by many industries to manage risk and reduce accidents. In the auto insurance industry, insurers traditionally calculate premiums by analyzing past claims reported by the insured policy holders, and reward those drivers that do not report accidents with a no-claims bonus. This may be a rather incorrect approach to the assessment of accident risk, especially when the insured has suffered accidents but chooses not to make a claim so as not to lose the no-claims bonus. Near-miss events provide information that can lead to actuarial premium calculations in the auto insurance industry [1,2]
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