Kim Do-Gyeong, Washington Simon
Department of Civil Engineering and Engineering Mechanics, University of Arizona, Tucson, AZ 85712-0072, USA.
Accid Anal Prev. 2006 Nov;38(6):1094-100. doi: 10.1016/j.aap.2006.04.017. Epub 2006 Jun 5.
Crash prediction models are used for a variety of purposes including forecasting the expected future performance of various transportation system segments with similar traits. The influence of intersection features on safety have been examined extensively because intersections experience a relatively large proportion of motor vehicle conflicts and crashes compared to other segments in the transportation system. The effects of left-turn lanes at intersections in particular have seen mixed results in the literature. Some researchers have found that left-turn lanes are beneficial to safety while others have reported detrimental effects on safety. This inconsistency is not surprising given that the installation of left-turn lanes is often endogenous, that is, influenced by crash counts and/or traffic volumes. Endogeneity creates problems in econometric and statistical models and is likely to account for the inconsistencies reported in the literature. This paper reports on a limited-information maximum likelihood (LIML) estimation approach to compensate for endogeneity between left-turn lane presence and angle crashes. The effects of endogeneity are mitigated using the approach, revealing the unbiased effect of left-turn lanes on crash frequency for a dataset of Georgia intersections. The research shows that without accounting for endogeneity, left-turn lanes 'appear' to contribute to crashes; however, when endogeneity is accounted for in the model, left-turn lanes reduce angle crash frequencies as expected by engineering judgment. Other endogenous variables may lurk in crash models as well, suggesting that the method may be used to correct simultaneity problems with other variables and in other transportation modeling contexts.
碰撞预测模型有多种用途，包括预测具有相似特征的各交通系统路段未来的预期表现。由于与交通系统中的其他路段相比，交叉路口发生的机动车冲突和碰撞比例相对较高，因此人们对交叉路口特征对安全性的影响进行了广泛研究。特别是交叉路口左转车道的影响，在文献中呈现出好坏参半的结果。一些研究人员发现左转车道对安全有益，而另一些人则报告了其对安全的不利影响。考虑到左转车道的设置往往是内生的，即受碰撞次数和/或交通流量的影响，这种不一致并不奇怪。内生性在计量经济学和统计模型中会产生问题，很可能是文献中所报告的不一致的原因。本文报告了一种有限信息极大似然（LIML）估计方法，以弥补左转车道的存在与角度碰撞之间的内生性。使用该方法可减轻内生性的影响，揭示了佐治亚州交叉路口数据集中左转车道对碰撞频率的无偏影响。研究表明，若不考虑内生性，左转车道“似乎”会导致碰撞；然而，当在模型中考虑内生性时，左转车道会如工程判断所预期的那样降低角度碰撞频率。其他内生变量也可能潜藏在碰撞模型中，这表明该方法可用于纠正其他变量以及其他交通建模背景下的同时性问题。
