Highlights<'/> Exploring the relationships between drivers' familiarity and two-lane rural road accidents. A multi-level study
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Exploring the relationships between drivers' familiarity and two-lane rural road accidents. A multi-level study

机译:探索驾驶员的熟悉程度与两车道农村道路交通事故之间的关系。多层次研究

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HighlightsRelationships between road familarity/unfamiliarity and accidents were searched.A multi-level analysis was conducted, from a macroscopic to more detailed levels.Familiarity was mainly measured based on involved drivers’ distance from residence.Detailed analyses reveal the inquired relationships better than macro-indicators.Familiarity was confirmed as risk factor, unfamiliarity has some unclear aspects.AbstractPrevious research has suggested that drivers’ route familiarity/unfamiliarity (using different definitions of familiarity), and the interactions between familiar and unfamiliar drivers, may affect both the driving performances and the likelihood of road crashes. The purpose of this study is to provide a contribution in the search for relationships between familiarity and crashes by: 1) introducing a measure of familiarity based on the distance from residence; 2) analyzing a traffic and accident dataset referred to rural two-lane sections of the Norwegian highways E6 and E39; 3) using a multi-level approach, based on different perspectives, from a macro analysis to more detailed levels.In the macro analyses, the accident rates computed for different seasons and for different summer traffic variation rates (used as indicators of the share of familiar drivers in the flow) were performed. At the second level, a logistic regression model was used to explain the familiarity/unfamiliarity of drivers (based on their distance from residence), through variables retrieved from the database. In the last step, an in-depth analysis considering also accident types and dynamics was conducted.In the macro analysis, no differences were found between accident rates in the different conditions. Whereas, as emerged from the detailed analyses, the factors: high traffic volume, low summer traffic variation, autumn/winter, minor intersections/driveways, speed limits <80 km/h, travel purposes (commutingot working) are associated to higher odds of having familiar drivers involved in crashes; while the factors: high traffic volume, high summer traffic variation, summer, head on/rear end-angle crashes, heavy vehicles involved, travel purposes (not commuting), young drivers involved are associated to higher odds of finding unfamiliar drivers involved. To a minor extent, some indications arise from the in-depth analyses about crash types and dynamics, especially for familiar drivers.With regard to the definitions used in this article, the familiarity was confirmed as an influential factor on the accident risk, possibly due to distraction and dangerous behaviors, while the influence of being unfamiliar on the accident proneness has some unclarified aspects. However, crashes to unfamiliar drivers may cluster at sites showing high summer traffic variation and in summer months.
机译: 突出显示 搜索道路熟悉/陌生与事故之间的关系。 进行了从宏观到更详细的多级分析。 熟悉度 详细分析显示了与宏观指标相比,查询关系更好。 已确认熟悉是危险因素,不熟悉有一些不清楚的方面。 摘要 先前的研究表明,驾驶员对路线的熟悉程度/不熟悉(使用不同的熟悉度定义)以及熟悉和不熟悉的驾驶员之间的交互作用,都可能影响驾驶性能和发生道路交通事故的可能性。这项研究的目的是通过以下方式为寻找熟悉程度与撞车之间的关系做出贡献:1)根据距居住地点的距离引入一种衡量熟悉程度的方法; 2)分析涉及挪威高速公路E6和E39的农村两车道部分的交通和事故数据集; 3)基于不同的角度,从宏观分析到更详细的级别使用多级方法。 进行宏分析,计算出不同季节和不同夏季交通变化率的事故率(用作交通中熟悉的驾驶员所占份额的指标)。在第二级,通过从数据库中检索的变量,使用逻辑回归模型来解释驾驶员的熟悉/不熟悉(基于他们与居住地的距离)。在最后一步中,进行了还考虑了事故类型和动态的深入分析。 在宏分析中,没有发现在不同情况下的事故率之间存在差异。而从详细分析中得出的因素有:较高的交通量,较低的夏季交通变化,秋季/冬季,较小的十字路口/车道,限速<80 关于本文中使用的定义,熟悉程度被确认为事故风险的影响因素,可能是由于分心和危险行为所致,而陌生对事故倾向性的影响在某些方面尚不清楚。但是,陌生驾驶员的撞车事故可能会聚集在夏季交通变化较大的地区和夏季。

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