Introduction: Recently the Federal Railroad Administration (FRA) released a new model for accident prediction at railroad grade crossings using a Zero Inflated Negative Binomial (ZINB) model with Empirical Bayes (EB) adjustments for accident history (2). This new model is adopted from the work that was conducted by the authors (3–6). The unique feature of the new FRA model is that it has a single equation for all three warning devices (crossbuck, flashing light, and gates) and uses the same variables regardless of the warning devices at the crossing. Since the New FRA model incorporates the warning device category as one of the variables in its model equation, the predicted accident frequency is higher when a crossing has crossbucks than flashing lights, and higher when it has flashing lights than gates. While this model is significantly better than the old USDOT model (7), its shortcoming is that the single equation does not accurately represent the field condition. Method: This paper presents the ZINEBS model (Zero Inflated Negative binomial with Empirical Bayes adjustment System). The ZINEBS model gives three different equations depending on the type of warning device used at the crossings (gates, flashing lights, and crossbucks). The three equations use variables, some of which are common across all warning devices, while other variables are specific to a warning device. The predicted values for the ZINEBS model show a closer agreement with the field data than the new FRA model. This observation was true for all three warning device types analyzed. Practical Applications: Based on the results of this study, the ZINEBS compliments the new FRA model and should be used when the single equation is not adequately representing the role of traffic control device types and relevant variables associated with that device type.

介绍：最近，联邦铁路管理局 (FRA) 发布了一种新的铁路平交道口事故预测模型，该模型使用零膨胀负二项式 (ZINB) 模型对事故历史进行经验贝叶斯 (EB) 调整 (2)。这种新模型是从作者（3-6）进行的工作中采用的。新的 FRA 模型的独特之处在于它对所有三个警告装置（交叉降压、闪光灯和闸门）都有一个方程，并且无论交叉路口的警告装置如何，它都使用相同的变量。由于新的 FRA 模型将警告装置类别作为其模型方程中的变量之一，当交叉口有交叉路口时，预测事故频率高于闪光灯，而当它有闪光灯时，预测事故频率高于大门。方法：本文介绍了 ZINEBS 模型（带有经验贝叶斯调整系统的零膨胀负二项式）。ZINEBS 模型给出了三个不同的方程，具体取决于交叉口处使用的警告装置的类型（大门、闪光灯和 crossbucks）。这三个等式使用变量，其中一些变量在所有警告设备中都是通用的，而其他变量则特定于警告设备。与新的 FRA 模型相比，ZINEBS 模型的预测值与现场数据更接近。对于所分析的所有三种警告装置类型，这一观察结果都是正确的。实际应用：根据这项研究的结果，ZINEBS 补充了新的 FRA 模型，当单个方程不能充分代表交通控制设备类型和与该设备类型相关的相关变量的作用时，应使用 ZINEBS。