Keall Michael D, Frith William J
Department of Public Health, Wellington School of Medicine and Health Sciences, University of Otago, Wellington, New Zealand.
Traffic Inj Prev. 2006 Sep;7(3):248-55. doi: 10.1080/15389580600672836.
It has been noted by several authors that risk (defined only in terms of total expected numbers of crash involvements per total distance driven) paints a misleading picture of crash liability, particularly for the young and the old, as their high risk is associated with risky driving patterns typical of people who drive low annual kms. This article sets out to analyze these driving patterns of low-km drivers and to evaluate the risk of these patterns. As licensing programs tend to focus on young and old drivers, who tend to drive lower annual distances, income and employment data are also analyzed for low-km drivers. This is to provide a better picture for policy makers of the sort of people and the sorts of transportation requirements that their policies may affect.
Crash data and travel data were disaggregated by driver characteristics and by driving conditions (road type, day and night, weekend and weekday) and combined to form estimates of risk for typical driving patterns of driver groups. Characteristics of driving patterns and of the drivers themselves were derived for groups defined by age and by the amount of annual driving undertaken.
Older drivers who drive less tend to have higher risk per km mainly due to their predominantly urban trips. Nevertheless, because older drivers on average manage to reduce their risk per distance driven by choosing driving patterns that are safer than the driving patterns of other age groups, the risk of older drivers as a group is not overestimated.
Despite being quite different from one another, the low- and high-km driving patterns of younger drivers were found to impose identical risks.
几位作者已经指出，风险（仅根据每总行驶距离的撞车事故总预期数量来定义）对撞车责任的描述具有误导性，特别是对于年轻人和老年人而言，因为他们的高风险与每年行驶里程较低的人典型的危险驾驶模式相关。本文旨在分析低里程驾驶者的这些驾驶模式，并评估这些模式的风险。由于驾照发放项目往往侧重于年轻人和老年人，他们每年的行驶距离往往较短，因此还对低里程驾驶者的收入和就业数据进行了分析。这是为了让政策制定者更好地了解其政策可能影响的人群类型以及交通需求类型。
撞车数据和出行数据按驾驶员特征和驾驶条件（道路类型、白天和黑夜、周末和平日）进行分类，并结合起来形成对驾驶员群体典型驾驶模式风险的估计。根据年龄和每年行驶里程对群体进行定义，得出驾驶模式以及驾驶员自身的特征。
行驶里程较少的老年驾驶员每公里的风险往往较高，主要是因为他们主要在城市出行。然而，由于老年驾驶员平均通过选择比其他年龄组更安全的驾驶模式来降低每行驶距离的风险，所以作为一个群体，老年驾驶员的风险并没有被高估。
尽管年轻驾驶员的低里程和高里程驾驶模式彼此差异很大，但发现它们带来的风险是相同的。
