Newmark Civil Engineering Lab, 205 N Mathews Ave., University of Illinois at Urbana-Champaign, Illinois 61820, United States.
J Safety Res. 2021 Dec;79:211-236. doi: 10.1016/j.jsr.2021.09.003. Epub 2021 Sep 25.
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.
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.
最近，联邦铁路管理局（FRA）发布了一种新的铁路道口事故预测模型，该模型使用具有经验贝叶斯（EB）调整的零膨胀负二项式（ZINB）模型来预测事故历史（2）。该新模型是由作者（3-6）开展的工作改编而来。FRA 新模型的独特之处在于，它为所有三种警告装置（道口栏、闪光灯和道口门）提供了一个单一的方程，并使用相同的变量，而与道口的警告装置无关。由于新的 FRA 模型将警告装置类别作为其模型方程中的一个变量，因此当道口具有道口栏时，预测事故频率会更高，当具有闪光灯时，预测事故频率会更高，而当具有闪光灯时，预测事故频率会更高。虽然该模型明显优于旧的 USDOT 模型（7），但其缺点是单一方程不能准确地反映现场条件。
本文介绍了 ZINEBS 模型（具有经验贝叶斯调整系统的零膨胀负二项式）。ZINEBS 模型根据道口使用的警告装置类型（道口门、闪光灯和道口栏）提供了三个不同的方程。这三个方程使用变量，其中一些变量在所有警告装置中通用，而其他变量则特定于一种警告装置。ZINEBS 模型的预测值与现场数据更吻合，而与新的 FRA 模型相比，这一观察结果适用于所有三种分析的警告装置类型。实际应用：基于这项研究的结果，ZINEBS 补充了新的 FRA 模型，当单一方程不能充分代表交通控制装置类型的作用和与该装置类型相关的相关变量时，应该使用 ZINEBS。
