Identifying the principal factors influencing traffic safety on interstate highways
Identifying the principal factors influencing traffic safety on interstate highways
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DOI：
10.1007/s42452-019-1796-2
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发表时间：
2019-12-01
影响因子：
2.6
通讯作者：
Hasan, Mahbub
中科院分区：
文献类型：
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作者：
Kassu, Aschalew;Hasan, Mahbub
本研究的目的是确定主要因素影响致命的，非致命的伤害和非伤害交通事故的城市和农村州际公路路段使用的统计方法称为主成分分析。最初，14个解释变量，包括路段长度，年平均日交通量（AADT），工作日/周末，一天中的小时，城乡指定的路段，中间类型，路面状况，道路几何特征，天气，车道数，驾驶员的年龄和性别，以及事故发生的年份。对三个碰撞类别和汇总数据集进行单独的主成分分析。分析的结果表明，无论使用的碰撞类别，七个主成分占70%以上的原始数据集的方差被保留。除了原始数据集的整体维度减少了50%之外，结果表明，在高速公路路段上观察到的事故类型的类别中，导致碰撞的关键变量是微不足道的。因子PC 1、PC 2、PC 3和PC 5的保留主成分载荷揭示了车道数、中值类型和段的AADT高度相关并由第一因子（PC 1）表示的事实。同样地，其他相关因素，例如当时的天气和路面状况（潮湿、干燥、积雪）、一天中的时间和照明状况、驾驶员的年龄和性别，分别由PC 2、PC 3和PC 5很好地表示。
This study aims at identifying the principal factors influencing fatal, nonfatal injury and non-injury traffic crashes on urban and rural interstate highway segments using a statistical approach called principal component analysis. Initially, fourteen explanatory variables including segment length, annual average daily traffic (AADT), weekday/weekend, hour of the day, urban-rural designation of the segment, median type, pavement surface condition, roadway geometric characteristics, weather, number of lanes, and drivers' age and gender, and the accident year were considered. Separate principal component analyses are performed for the three crash categories and the aggregate dataset. The results of the analyses show that, regardless of the crash categories used, seven principal components accounting for over 70% of the variances in the original datasets were retained. In addition to the overall dimensional reduction of the original dataset by 50%, the results suggest that the key variables contributing to the crashes across the categories of the accident types observed on the freeway segments are insignificant. The retained principal component loadings of the factors PC1, PC2, PC3, and PC5 revealed the fact that the number of lanes, the median type, and the AADT of the segments are highly correlated and represented by the first factor (PC1). Similarly, other interrelated factors such as the prevailing weather and the pavement surface condition (wet, dry, snow), the hour of the day and the lighting condition, the drivers' age and gender are well represented by PC2, PC3, and PC5 respectively.