Schneider Robert J, Ryznar Rhonda M, Khattak Asad J
Department of City and Regional Planning, University of North Carolina at Chapel Hill, CB 3140 New East Bldg., Chapel Hill, NC 27599-3140, USA.
Accid Anal Prev. 2004 Mar;36(2):193-211. doi: 10.1016/s0001-4575(02)00149-5.
There are about 75,000 pedestrian crashes in the United States each year. Approximately 5000 of these crashes are fatal, accounting for 12% of all roadway deaths. On college campuses, pedestrian exposure and crash-risk can be quite high. Therefore, we analyzed pedestrian crashes on the campus of the University of North Carolina at Chapel Hill (UNC) as a test case for our spatially-oriented prototype tool that combines perceived-risk (survey) data with police-reported crash data to obtain a more complete picture of pedestrian crash-risk. We use spatial analysis techniques combined with regression models to understand factors associated with risk. The spatial analysis is based on comparing two distributions, i.e. the locations of perceived-risk with police-reported crash locations. The differences between the two distributions are statistically significant, implying that certain locations on campus are perceived as dangerous, though pedestrian crashes have not yet occurred there, and there are actual locations of police-reported crashes that are not perceived to be dangerous by pedestrians or drivers. Furthermore, we estimate negative binomial regression models to combine pedestrian and automobile exposure with roadway characteristics and spatial/land use information. The models show that high exposure, incomplete sidewalks and high crosswalk density are associated with greater observed and perceived pedestrian crash-risk. Additionally, we found that people perceive a lower risk near university libraries, stadiums, and academic buildings, despite the occurrence of crashes.
美国每年约有75000起行人交通事故。其中约5000起是致命事故，占所有道路死亡人数的12%。在大学校园里，行人暴露率和事故风险可能相当高。因此，我们分析了北卡罗来纳大学教堂山分校（UNC）校园内的行人交通事故，以此作为我们的空间导向原型工具的测试案例，该工具将感知风险（调查）数据与警方报告的事故数据相结合，以更全面地了解行人事故风险。我们使用空间分析技术结合回归模型来了解与风险相关的因素。空间分析基于比较两种分布，即感知风险地点与警方报告的事故地点。这两种分布之间的差异具有统计学意义，这意味着校园内某些地点被认为是危险的，尽管那里尚未发生行人交通事故，而且警方报告的事故实际发生地点并未被行人或司机视为危险地点。此外，我们估计了负二项回归模型，将行人和汽车暴露情况与道路特征以及空间/土地利用信息相结合。模型显示，高暴露率、不完整的人行道和高人行横道密度与更高的观察到的和感知到的行人事故风险相关。此外，我们发现，尽管发生了事故，但人们在大学图书馆、体育场和学术楼附近感知到的风险较低。
