Department of Civil and Environmental Engineering, South Dakota State University, CEH 148, Box 2219, Brookings, SD 57007, United States.
Accid Anal Prev. 2010 Nov;42(6):1531-7. doi: 10.1016/j.aap.2010.03.009. Epub 2010 Apr 24.
Identifying locations that exhibit the greatest potential for safety improvements is becoming more and more important because of competing needs and a tightening safety improvement budget. Current crash modeling practices mainly target changes at the mean level. However, crash data often have skewed distributions and exhibit substantial heterogeneity. Changes at mean level do not adequately represent patterns present in the data. This study employs a regression technique known as the quantile regression. Quantile regression offers the flexibility of estimating trends at different quantiles. It is particularly useful for summarizing data with heterogeneity. Here, we consider its application for identifying intersections with severe safety issues. Several classic approaches for determining risk-prone intersections are also compared. Our findings suggest that relative to other methods, quantile regression yields a sensible and much more refined subset of risk-prone locations.
由于竞争需求和安全改善预算的收紧，确定具有最大安全改善潜力的地点变得越来越重要。目前的碰撞建模实践主要针对均值水平的变化。然而，碰撞数据通常具有偏态分布且表现出很大的异质性。均值水平的变化不能充分代表数据中存在的模式。本研究采用了一种称为分位数回归的回归技术。分位数回归提供了在不同分位数处估计趋势的灵活性。它对于总结具有异质性的数据特别有用。在这里，我们考虑将其应用于识别具有严重安全问题的交叉口。还比较了几种用于确定高风险交叉口的经典方法。我们的研究结果表明，与其他方法相比，分位数回归产生了一个合理且更加精细的高风险位置子集。
