首页> 外文期刊>Journal of studies on alcohol and drugs. >Drinking, driving, and crashing: a traffic-flow model of alcohol-related motor vehicle accidents.
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Drinking, driving, and crashing: a traffic-flow model of alcohol-related motor vehicle accidents.

机译:酒后驾驶和撞车:酒精相关机动车事故的交通流模型。

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OBJECTIVE: This study examined the influence of on-premise alcohol-outlet densities and of drinking-driver densities on rates of alcohol-related motor vehicle crashes. A traffic-flow model is developed to represent geographic relationships between residential locations of drinking drivers, alcohol outlets, and alcohol-related motor vehicle crashes. METHOD: Cross-sectional and time-series cross-sectional spatial analyses were performed using data collected from 144 geographic units over 4 years. Data were obtained from archival and survey sources in six communities. Archival data were obtained within community areas and measured activities of either the resident population or persons visiting these communities. These data included local and highway traffic flow, locations of alcohol outlets, population density, network density of the local roadway system, and single-vehicle nighttime (SVN) crashes. Telephone-survey data obtained from residents of the communities were used to estimate the size of the resident drinking and driving population. RESULTS: Cross-sectional analyses showed that effects relating on-premise densities to alcohol-related crashes were moderated by highway trafficflow. Depending on levels of highway traffic flow, 10% greater densities were related to 0% to 150% greater rates of SVN crashes. Time-series cross-sectional analyses showed that changes in the population pool of drinking drivers and on-premise densities interacted to increase SVN crash rates. CONCLUSIONS: A simple traffic-flow model can assess the effects of on-premise alcohol-outlet densities and of drinking-driver densities as they vary across communities to produce alcohol-related crashes. Analyses based on these models can usefully guide policy decisions on the sitting of on-premise alcohol outlets.
机译:目的:本研究探讨了内部酒精出口密度和酒后驾驶密度对酒精相关的机动车碰撞率的影响。开发了一种交通流模型,以表示饮酒驾驶员,酒精饮料店和与酒精有关的机动车撞车的居住地点之间的地理关系。方法:采用从144个地理单位收集的4年数据进行横截面和时间序列横截面空间分析。数据来自六个社区的档案和调查来源。在社区区域内获取档案数据,并测量居民或访问这些社区的人员的活动。这些数据包括本地和高速公路的交通流量,酒精出口的位置,人口密度,本地道路系统的网络密度以及单车夜间(SVN)崩溃。从社区居民获得的电话调查数据被用来估计居民饮酒和驾车人口的规模。结果:横断面分析表明,与公路交通相关的交通流量减轻了内部密度与酒精相关碰撞的影响。根据高速公路的交通流量水平,密度增加10%与SVN崩溃率增加0%至150%有关。时间序列的横截面分析表明,酒后驾驶人口总数的变化和企业内部密度相互影响,从而增加了SVN崩溃率。结论:一个简单的交通流模型可以评估内部酒精出口密度和酒后驾车者密度的影响,因为它们在社区之间变化以产生与酒精相关的车祸。基于这些模型的分析可以有效地指导有关内部酒精饮料销售点的政策决策。

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