Quantitative risk assessment of freeway crash casualty using high-resolution traffic data

Quantitative risk assessment of freeway crash casualty using high-resolution traffic data
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使用高分辨率交通数据对高速公路事故伤亡进行定量风险评估

DOI:
10.1016/j.ress.2017.09.005
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发表时间:
2018
影响因子:
8.1
通讯作者:
Yong Wang
Yong Wang
中科院分区:
工程技术1区
文献类型:
--
作者:
Chengcheng Xu;Yong Wang

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利用高分辨率交通数据,研究交通流条件对不同类型碰撞事故伤亡的影响。主成分分析进行处理大量的相关车道特定的交通变量。一个四阶段的随机参数序贯逻辑回归模型,然后开发链接的碰撞伤亡的概率,每种碰撞类型与实时交通流量,天气和几何条件。结果表明,侧擦碰撞中的伤害风险随着相邻车道之间的速度差、右侧车道上的交通量和内侧车道上的交通量标准差的增加而增加。拥挤的交通状况及其与恶劣天气的相互作用降低了侧擦碰撞中受伤的风险。追尾事故中,分流区交通拥挤,不利天气条件下上下游车站右车道速度差大,是造成事故伤亡的主要原因。此外,内侧车道上的高容量减少了追尾事故中受伤的风险。验证结果表明,预测精度在每个严重程度的碰撞类型是令人满意的。
This study aimed to investigate the impacts of traffic flow conditions on crash casualty of different collision types using high-resolution traffic data. The principle components analysis was conducted to deal with a large number of correlated lane-specific traffic variables. A four-stage random-parameters sequential logistic regression model was then developed to link the probability of crash casualty of each collision type with real-time traffic flow, weather, and geometric conditions. The results showed that the risks of injuries in sideswipe crashes increase with an increase in the speed difference between adjacent lanes, volume on right lane, and standard deviation of volume on inner lanes. The congested traffic conditions and its interaction with adverse weather decrease the risks of injuries in sideswipe crashes. For rear-end crashes, the congested traffic conditions at diverge area, and large difference in speed on right lane between upstream and downstream station in adverse weather contribute to crash casualty. Moreover, high volume on inner lanes reduce the risks of injuries in rear-end crashes. The validation results showed that the prediction accuracy at each severity level by collision types is satisfactory.
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