Multilevel models for evaluating the risk of pedestrian-motor vehicle collisions at intersections and mid-blocks.
Multilevel models for evaluating the risk of pedestrian-motor vehicle collisions at intersections and mid-blocks.
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DOI：
10.1016/j.aap.2015.08.013
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发表时间：
2015-11
期刊：
影响因子：
--
通讯作者：
Rivara FP
中科院分区：
文献类型：
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作者：
Quistberg DA;Howard EJ;Ebel BE;Moudon AV;Saelens BE;Hurvitz PM;Curtin JE;Rivara FP
步行是一种流行的体育活动形式，对健康有明显的好处。促进行人安全步行需要评估特定道路位置的行人与机动车碰撞的风险，以确定可能需要改进道路和其他干预措施的地方。本分析的目的是估计华盛顿州西雅图十字路口和街区中部发生行人碰撞的风险。该研究使用了来自警方报告的 2007 年至 2013 年行人与机动车碰撞数据以及十字路口和街区中间位置的微环境和宏观环境的详细特征。主要结果是随时间推移每个地点的行人与机动车碰撞次数（事故率 [IRR] 和 95% 置信区间 [95% CI]）。多级混合效应泊松模型解释了位置和人口普查区块内部和之间随时间变化的相关性。分析考虑了行人和车辆活动（例如住宅密度和道路分类）。在最终的多变量模型中，与中间街区相比，具有 4 个路段或 5 个或更多路段的交叉路口的行人碰撞率更高。非住宅区道路的碰撞率明显高于住宅区道路，其中主要干道的碰撞率最高。街道宽度每 10 英尺，行人碰撞率就会增加 9%。有交通信号的地点的碰撞率是无信号地点的两倍，而有明显人行横道的地点的碰撞率也更高。有明显人行横道的地方发生碰撞的风险也较高。有单向道路或有鼓励驾车者将通行权让给行人的标志的地点，行人碰撞事故较少。在鼓励更多行人活动（更多使用公共汽车、更多快餐店、更高的就业、住宅和人口密度）的地点，碰撞率更高。十字路口密度较高的地点的碰撞率较低，而住宅物业价值较高的地区的碰撞率也较低。采用的新颖时空方法将道路/十字路口特征与周围社区特征相结合，应有助于城市机构更好地识别高风险地点，以供进一步研究和分析。改善道路并提高行人的安全性可以实现减少行人碰撞和促进身体活动的公共卫生目标。
Walking is a popular form of physical activity associated with clear health benefits. Promoting safe walking for pedestrians requires evaluating the risk of pedestrian-motor vehicle collisions at specific roadway locations in order to identify where road improvements and other interventions may be needed. The objective of this analysis was to estimate the risk of pedestrian collisions at intersections and mid-blocks in Seattle, WA. The study used 2007-2013 pedestrian-motor vehicle collision data from police reports and detailed characteristics of the microenvironment and macroenvironment at intersection and mid-block locations. The primary outcome was the number of pedestrian-motor vehicle collisions over time at each location (incident rate ratio [IRR] and 95% confidence interval [95% CI]). Multilevel mixed effects Poisson models accounted for correlation within and between locations and census blocks over time. Analysis accounted for pedestrian and vehicle activity (e.g., residential density and road classification). In the final multivariable model, intersections with 4 segments or 5 or more segments had higher pedestrian collision rates compared to mid-blocks. Non-residential roads had significantly higher rates than residential roads, with principal arterials having the highest collision rate. The pedestrian collision rate was higher by 9% per 10 feet of street width. Locations with traffic signals had twice the collision rate of locations without a signal and those with marked crosswalks also had a higher rate. Locations with a marked crosswalk also had higher risk of collision. Locations with a one-way road or those with signs encouraging motorists to cede the right-of-way to pedestrians had fewer pedestrian collisions. Collision rates were higher in locations that encourage greater pedestrian activity (more bus use, more fast food restaurants, higher employment, residential, and population densities). Locations with higher intersection density had a lower rate of collisions as did those in areas with higher residential property values. The novel spatiotemporal approach used that integrates road/crossing characteristics with surrounding neighborhood characteristics should help city agencies better identify high-risk locations for further study and analysis. Improving roads and making them safer for pedestrians achieves the public health goals of reducing pedestrian collisions and promoting physical activity.