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Abstract
Several studies have indicated that the risk of driver and passenger fatality associated with motorcycle accidents far exceeds that of automobile accidents. This alarming statistic has caused increased attention from state motor vehicle departments, state health departments and the Insurance industry to pursue efforts to introduce programs to dramatically reduce motorcycle accidents. For example, Connecticut introduced mandatory training courses for safety motorcycle driving. It is the purpose of this paper to develop a unifying framework to quantify the effectiveness of such mandatory programs and to translate this in terms of a possible insurance rate reduction. To calibrate the proposed framework, we used insurance claims data drawn from states where such mandatory programs have been introduced and where typically there are two types: a Basic (BRC) or an Advanced Motorcycle Safety Course (ARC). An Integer-Valued Autoregressive (INAR) https://www.51lunwen.org/FinancialMathematics/2012/0223/1058095655.html model is employed to assess the claims frequency of motorcycle accidents taking into account the differences in the type of training program. A heterogeneity factor is injected into the model to assess the impact of the training programs. Finally, the model allows us to estimate incentive adjustment factors to reflect possible insurance rate reduction as a result of the mandatory program.

References
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