Department of Civil, Environmental, Building Engineering and Chemistry, Technical University of Bari, Via Orabona 4, 70125 Bari, Italy.
Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Høgskoleringen 7, 7491 Trondheim, Norway.
Accid Anal Prev. 2018 Feb;111:280-296. doi: 10.1016/j.aap.2017.11.013. Epub 2017 Dec 15.
Previous 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 (commuting/not 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.
先前的研究表明，驾驶员对路线的熟悉程度（使用不同的熟悉度定义）以及熟悉和不熟悉驾驶员之间的相互作用，可能会影响驾驶表现和道路事故的发生概率。本研究旨在通过以下方法为寻找熟悉度与事故之间的关系做出贡献：1）引入基于与居住地距离的熟悉度衡量标准；2）分析涉及挪威 E6 和 E39 高速公路的农村双车道路段的交通和事故数据集；3）基于不同视角，采用多层次方法，从宏观分析到更详细的层面进行分析。在宏观分析中，针对不同季节和不同夏季交通变化率（用作流中熟悉驾驶员比例的指标）计算了事故率。在第二级，使用逻辑回归模型来解释驾驶员的熟悉度/不熟悉度（基于其与居住地的距离），通过从数据库中检索的变量进行解释。在最后一步，考虑事故类型和动态进行了深入分析。在宏观分析中，不同条件下的事故率没有差异。然而，从详细分析中得出的结果表明，高交通量、低夏季交通变化、秋季/冬季、次要交叉口/车道、限速<80km/h、旅行目的（通勤/不工作）与熟悉驾驶员参与事故的几率较高相关；而高交通量、高夏季交通变化、夏季、正面/追尾碰撞、重型车辆参与、非通勤旅行目的、年轻驾驶员参与与不熟悉驾驶员参与事故的几率较高相关。在深入分析中，特别是对于熟悉的驾驶员，从事故类型和动态方面得出了一些不太明显的结果。就本文使用的定义而言，熟悉度被确认为对事故风险的影响因素，可能是由于分心和危险行为所致，而不熟悉度对事故易发性的影响存在一些不明确的方面。然而，不熟悉的驾驶员发生事故可能会集中在夏季交通变化率高和夏季月份的地点。
