Department of Civil Engineering, Indian Institute of Technology Madras, Chennai-600036, India.
J Safety Res. 2011 Feb;42(1):39-42. doi: 10.1016/j.jsr.2010.11.007. Epub 2011 Jan 22.
Generalized linear modeling (GLM), with the assumption of Poisson or negative binomial error structure, has been widely employed in road accident modeling. A number of explanatory variables related to traffic, road geometry, and environment that contribute to accident occurrence have been identified and accident prediction models have been proposed. The accident prediction models reported in literature largely employ the fixed parameter modeling approach, where the magnitude of influence of an explanatory variable is considered to be fixed for any observation in the population. Similar models have been proposed for Indian highways too, which include additional variables representing traffic composition. The mixed traffic on Indian highways comes with a lot of variability within, ranging from difference in vehicle types to variability in driver behavior. This could result in variability in the effect of explanatory variables on accidents across locations. Random parameter models, which can capture some of such variability, are expected to be more appropriate for the Indian situation.
The present study is an attempt to employ random parameter modeling for accident prediction on two-lane undivided rural highways in India. Three years of accident history, from nearly 200 km of highway segments, is used to calibrate and validate the models.
The results of the analysis suggest that the model coefficients for traffic volume, proportion of cars, motorized two-wheelers and trucks in traffic, and driveway density and horizontal and vertical curvatures are randomly distributed across locations.
The paper is concluded with a discussion on modeling results and the limitations of the present study.
广义线性模型（GLM），假设泊松或负二项式误差结构，已广泛应用于道路事故建模。已经确定了许多与交通、道路几何形状和环境有关的解释变量，这些变量促成了事故的发生，并提出了事故预测模型。文献中报告的事故预测模型主要采用固定参数建模方法，其中解释变量的影响程度被认为是在总体中的任何观测值上都是固定的。印度的高速公路也提出了类似的模型，其中包括代表交通组成的附加变量。印度高速公路上的混合交通存在很多内部差异，包括车辆类型的差异和驾驶员行为的可变性。这可能导致解释变量对不同地点事故的影响具有可变性。可以捕捉到部分此类可变性的随机参数模型，预计更适合印度的情况。
本研究试图在印度的双车道非分隔农村公路上应用随机参数建模进行事故预测。使用近 200 公里的公路段的三年事故历史数据来校准和验证模型。
分析结果表明，交通量、汽车比例、机动两轮车和卡车在交通中的比例、车道密度以及水平和垂直曲率的模型系数在位置上是随机分布的。
本文讨论了建模结果和本研究的局限性。
