Department of Civil Engineering, University of Colorado Denver, 1200 Larimer Street, Campus Box 113, Denver, CO 80217, United States.
Accid Anal Prev. 2011 May;43(3):769-81. doi: 10.1016/j.aap.2010.10.024. Epub 2010 Dec 18.
Negative binomial regression models were used to assess the effect of street and street network characteristics on total crashes, severe injury crashes, and fatal crashes. Data from over 230,000 crashes taking place over 11 years in 24 California cities was analyzed at the U.S. Census Block Group level of geography. In our analysis we controlled for variables such as vehicle volumes, income levels, and proximity to limited access highways and to the downtown area. Street network characteristics that were considered in the analysis included street network density and street connectivity along with street network pattern. Our findings suggest that for all levels of crash severity, street network characteristics correlate with road safety outcomes. Denser street networks with higher intersection counts per area are associated with fewer crashes across all severity levels. Conversely, increased street connectivity as well as additional travel lanes along the major streets correlated with more crashes. Our results suggest that in assessing safety, it is important to move beyond the traditional approach of just looking at the characteristics of the street itself and examine how the interrelated factors of street network characteristics, patterns, and individual street designs interact to affect crash frequency and severity.
我们使用负二项回归模型来评估街道和街道网络特征对总事故、严重伤害事故和致命事故的影响。在美国人口普查街区组层面上，对 24 个加利福尼亚城市 11 年间发生的超过 23 万起事故的数据进行了分析。在我们的分析中，我们控制了车辆数量、收入水平以及与高速公路和市中心的接近程度等变量。分析中考虑的街道网络特征包括街道网络密度和街道连通性以及街道网络模式。我们的研究结果表明，在所有严重程度的事故中，街道网络特征与道路安全结果相关。每单位面积交叉口数量较多的街道网络密度较高与所有严重程度级别的事故较少有关。相反，主要街道上的连通性增加以及额外的行车道与更多的事故相关。我们的研究结果表明，在评估安全性时，重要的是要超越仅仅关注街道本身特征的传统方法，研究街道网络特征、模式和个别街道设计的相互关联因素如何相互作用，以影响事故频率和严重程度。
