Hu Shou-Ren, Li Chin-Shang, Lee Chi-Kang
Assistant Professor, Department of Transportation and Communication Management Science, National Cheng Kung University, Tainan City 701, Taiwan.
Saf Sci. 2010 Feb 1;48(2):186-194. doi: 10.1016/j.ssci.2009.07.010.
Although several studies have used logit or probit models and their variants to fit data of accident severity on roadway segments, few have investigated accident severity at a railroad grade crossing (RGC). Compared to accident risk analysis in terms of accident frequency and severity of a highway system, investigation of the factors contributing to traffic accidents at an RGC may be more complicated because of additional highway-railway interactions. Because the proportional odds assumption was violated while fitting cumulative logit modeled by the proportional odds models with stepwise variable selection to ordinal accident severity data collected at 592 RGCs in Taiwan, as suggested by Strokes et al. (2000, p. 249) a generalized logit model with stepwise variable selection was used instead to identify explanatory variables (factors or covariates) that were significantly associated with the severity of collisions. Hence, the fitted model was used to predict the level of accident severity, given a set of values in the explanatory variables. Number of daily trains, highway separation, number of daily trucks, obstacle detection device, and approaching crossing markings significantly affected levels of accident severity at an RGC (p-value = 0.0009, 0.0008, 0.0112, 0.0017, and 0.0003, respectively). Finally, marginal effect analysis on the number of daily trains and law enforcement camera was conducted to evaluate the effect of the number of daily trains and presence of a law enforcement camera on the potential accident severity.
尽管已有多项研究使用逻辑回归或概率单位模型及其变体来拟合道路路段事故严重程度的数据，但很少有研究调查铁路平交道口（RGC）的事故严重程度。与公路系统事故频率和严重程度方面的事故风险分析相比，由于存在额外的公路 - 铁路相互作用，对导致铁路平交道口交通事故的因素进行调查可能更为复杂。正如Strokes等人（2000年，第249页）所指出的，在对台湾592个铁路平交道口收集的有序事故严重程度数据使用逐步变量选择的比例优势模型拟合累积逻辑回归时，比例优势假设被违反，因此使用了具有逐步变量选择的广义逻辑回归模型来识别与碰撞严重程度显著相关的解释变量（因素或协变量）。因此，在给定解释变量中的一组值的情况下，使用拟合模型来预测事故严重程度等级。每日列车数量、公路分隔、每日卡车数量、障碍物检测装置和接近道口标记对铁路平交道口的事故严重程度等级有显著影响（p值分别为0.0009、0.0008、0.0112、0.0017和0.0003）。最后，对每日列车数量和执法摄像头进行了边际效应分析，以评估每日列车数量和执法摄像头的存在对潜在事故严重程度的影响。
