# Dynamic Railway Derailment Risk Analysis with Text-Data-Based Bayesian Network
## 4 Case Study and Experimental Results  
In this study, the accident database is compiled based on 10-year railway equipment accident (REA) data from the Federal Railroad Administration (FRA) ([[http://www.fra.dot.gov](http://www.fra.dot.gov)]([http://www.fra.dot.gov](http://www.fra.dot.gov))), which include railway equipment and highway accident data, operational data and railroad casualty data. Train accidents are frequently the culmination of a sequence of events and a variety of conditions or circumstances that may have contributed to their occurrence, and they commonly meet certain reporting criteria. To better record and understand why an accident has occurred, a standard document named the Train Accident Cause Codes (TACC) is provided to describe the causal factors of the accident. In the TACC, there are 289 codes related to the causal factors of accidents. Figure 3 shows that the largest proportion is derailments, accounting for 59.32% of the 10-year records. Hence, the main input of our proposed method are the text data of train derailment accidents, which are collected by FRA. Here, we selected the cause factors of derailment accidents as input. Five kinds of factors are defined as follows: (I) rack, roadbed and structures; (2) signal and communication; (3) mechanical and electrical failures; (4) train operation--human factors; (5) miscellaneous causes, including environment conditions, management, decisions and measures. To overcome the limitation of the BN model that it cannot straightforwardly identify related barriers, we use graphical models to help find out the barrier. In the graphical models, barriers have many contribution factors. To better understand the roles of barriers, we organized a structure to build fault trees (FTs) which are designed to identify the causal factors as in [13] where a suitable method was provided to map fault trees into BNs. Usually, the prior possibilities of the BN model are hard to decide, so we used a text based  
Figure 3: Federal Railroad Administration (FRA) accidents disaggregated by accident type. method to obtain prior inputs from text data. Therefore, the great improvement of TBN is that a text-method was adopted to provide objective prior data for the assessment model. First, we introduced an experiment to illustrate the efficiency of the text-based method in TBN. In this experiment, we selected network-word-sentence (NWS) and the most frequency (MF) as the text-based methods. NWS was proposed in our previous paper and is based on complex network theory, and the MF method has exhibited a prominent performance in the literature [24] and is frequently used. Then, we compared these two methods in all three metrics: precise, recall and F-measure. The performance of the NWS method has a precision of 58.77%, a recall of 61.86% and an F-measure of 60.28%. The precision, recall and F-measure of TBN are 8.64%, 10.70% and 9.84% higher than the MF result respectively, which shows that the performance of NWS is better. Therefore, NWS was applied as the text-based method in TBN. Additionally, according to the literature in [12; 13; 14; 15; 16; 17; 18; 19], we provide a conclusion comparison in Table 2. The triangle symbols ('\(\bigtriangleup\)' and '\(\blacktriangle\)') in this table represent the ability and performance of a method, but the bold triangle ('\(\blacktriangle\)') means its performance is the best. The symbol '\(\times\)' represents that there is none of this feature in the method. For example, Figure 4 shows a Swiss cheese model to express the processes of barrier failure to cause a derailment, in which the relations are linear. While, Figure 5 demonstrates the practical non-linear relationship of barriers. Briefly, Figure 5 shows several advantages as follows, compared with the Swiss cheese model in Figure 4: (1) shows that some barriers (e.g., B2 and B3) work together instead of in a strict sequence, (2) could express a parallel relationship such as B4a and B4b, and (3) can skip a barrier when its previous barrier fails.