PubMed · 5798242
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H J Wanebo, T K Hunt, C Mathewson. 1969. Rectal injuries.. https://doi.org/10.1097/00005373-196908000-00009
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After teenage males, elderly individuals have the highest per capita motor vehicle fatality rate in the United States. Surprisingly, there has been only limited work examining the effect of state motor vehicle laws on older driver fatalities. This paper uses state-level data from the 1985-2000 Fatality Analysis Reporting System to examine the effects of changes in state laws dealing with license renewal, seatbelt use, speed limits, and driving while intoxicated on fatalities among drivers and others aged 65 and over. Negative binomial regressions are estimated using alternatively state and year fixed effects, or age and year fixed effects. In-person license renewal reduced fatalities among the oldest drivers, but vision tests, road tests and the length of the license renewal cycle generally did not. In terms of policies that apply to all drivers, seatbelt laws, particularly with primary enforcement, were generally the only policies that reduced older driver fatalities. These results are noteworthy because a number of policies that have been effective towards increasing younger driver safety are not relevant for older drivers, implying that policymakers must think broadly about using state laws to improve older driver safety.
Passenger restrictions for new teenage drivers that became law in 1998 in California provide an opportunity to study the effectiveness of such laws in reducing the number of passengers as well as the influence of teenage passengers on novice drivers. Using fatal and injury crash data from California's Statewide Integrated Traffic Records System, this study found that teenage passengers are a causal factor in crashes of 16-year-old drivers and that in the three years following implementation of the new law, the average number of teenage passengers carried by 16-year-olds decreased by approximately 25%. Without considering the beneficial effect of a decrease in the crash rate, the decrease in the number of teenage passengers in actual crashes resulted in an estimated saving of eight lives and the prevention of 684 injuries over a three-year period.
There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states-perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of "excess" zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to "excess" zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed-and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros.