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Identification of contributing factors for wrong-way crashes on freeways in Illinois

机译:确定伊利诺伊州高速公路错误撞车的成因

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摘要

To investigate the contributing factors for wrong-way crashes on freeways based on information available from crash databases and crash reports, causal tables and a Haddon matrix are used to analyze fatal and injury-related crashes caused by wrong-way driving from 2004 to 2009 in Illinois. For the first step, causal tables are generated based on the police crash reports, in which the percentage of crashes caused by each factor is calculated. Based on these percentages, the contributing factors are ranked. Next, a Haddon matrix is developed for each wrong-way crash in which different factors are identified separately for periods before, during, and after the crash. These factors include drivers' age, drivers' condition, crash causes, vehicle types, vehicle maneuvers, roadway conditions, weather, and wrong-way entry points. For a crash with multiple contributing factors, a weighted point is calculated and assigned to each factor. The total points for each factor are used to rank the order of all contributing factors. Furthermore, a significance test is used to analyze the statistical significance of each of those factors. The analysis results indicate that alcohol impairment, darkness, and driver's age and skill are among the top contributing factors for wrong-way crashes.
机译:为了根据事故数据库和事故报告中的信息调查高速公路错误事故的成因,我们使用因果表和Haddon矩阵分析了2004年至2009年因错误驾驶导致的致命和与伤害有关的事故。伊利诺伊州。第一步,根据警察事故报告生成因果表,其中计算每个因素导致的事故百分比。基于这些百分比,对影响因素进行排名。接下来,针对每个错车事故开发Haddon矩阵,其中在撞车之前,期间和之后的各个阶段分别确定不同的因素。这些因素包括驾驶员的年龄,驾驶员的状况,撞车原因,车辆类型,车辆操纵,道路状况,天气和错误的进入点。对于具有多个影响因素的碰撞,将计算一个加权点并将其分配给每个因素。每个因素的总分用于排列所有影响因素的顺序。此外,使用显着性检验来分析每个因素的统计显着性。分析结果表明,酗酒,黑暗,驾驶员的年龄和技能是导致错误行车事故的最主要因素。

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