Anastasopoulos Panagiotis Ch, Tarko Andrew P, Mannering Fred L
School of Civil Engineering, 550 Stadium Mall Drive, Purdue University, West Lafayette, IN 47907-2051, United States.
Accid Anal Prev. 2008 Mar;40(2):768-75. doi: 10.1016/j.aap.2007.09.006. Epub 2007 Oct 2.
There has been an abundance of research that has used Poisson models and its variants (negative binomial and zero-inflated models) to improve our understanding of the factors that affect accident frequencies on roadway segments. This study explores the application of an alternate method, tobit regression, by viewing vehicle accident rates directly (instead of frequencies) as a continuous variable that is left-censored at zero. Using data from vehicle accidents on Indiana interstates, the estimation results show that many factors relating to pavement condition, roadway geometrics and traffic characteristics significantly affect vehicle accident rates.
已有大量研究使用泊松模型及其变体（负二项式模型和零膨胀模型）来增进我们对影响路段事故频率因素的理解。本研究探索了一种替代方法——托比特回归的应用，即将车辆事故率直接视为一个在零处左删失的连续变量（而非事故频率）。利用印第安纳州州际公路上的车辆事故数据，估计结果表明，许多与路面状况、道路几何形状和交通特征相关的因素会显著影响车辆事故率。
