# Dynamic Railway Derailment Risk Analysis with Text-Data-Based Bayesian Network
###### Abstract  
In recent years, transportation system safety analysis has become increasingly challenging and highly demanding. Unstructured data contain sufficient information from which inherent interactions can be extracted. Determining how to process and fuse a large amount of unstructured data is a challenging task. In this paper, we propose a text-based Bayesian network (TBN) method to establish a Bayesian network (BN) based on text records, where the BN's arcs are obtained from barrier relationships identified by a graphical model and its prior probabilities stem from fault trees. The comparative experimental results illustrate that the text-based method in TBN is efficient. The precision, recall and F-measure of TBN are 8.64%, 10.70% and 9.84% higher, respectively, than the most frequent (MF) result. Moreover, compared to the traditional BN, whose prior probabilities are frequently acquired from experts, the prior probabilities of the proposed text-based BN (TBN) have a high confidence. The experimental results of a train derailment accident case study show that with changes in the train derailment probabilities and the safety potentials of the barriers, the TBN generates quantitative results and reveals the critical risks of derailment accidents. Additionally, this work demonstrates relevant nonlinear relationships to improve the assessment results. Therefore, based on text-based data, this study reveals that barrier safety analysis has the potential to identify high-risk barriers, which can guide managers to enhance these barriers. a 2021 11994  
cause control [4]. Moreover, systematic accident model-based methods are qualitative methods, not quantitative. To overcome the drawbacks of systematic models, the theory of complex networks has been commonly applied in transportation. In a complex network, an accident causation network is necessarily established. Causation is represented by nodes, and the relationship between two nodes is shown as a link. By applying statistical characteristics, various models have been introduced by the node degree. [PERSON] et al. established a network to analyze or assess the risks in railway systems [5]. Due to the lack of weighting in much of the work based on complex networks, [PERSON] et al. improved [PERSON]'s model. [PERSON] et al. proposed a complex network-based method that considers the weights of edges [6]. However, these studies have not considered cascading processes. To overcome this limitation, [PERSON] et al. proposed a railway fault spreading model based on the dynamics of the process of failure interactions in networks [7]. [PERSON] and [PERSON] developed a model based on a complex network for risk monitoring [4]. This model has been mainly employed to identify accident causal factors and to analyze how these factors influence each other. [PERSON] and [PERSON] [8] recommended a cascading failure model considering cascading processes. Many other quantitative models based on fault trees have also been introduced to analyze safety assessments. As a result that it is difficult to capture the failure probabilities of basic events based on past experience using fault trees, the fuzzy set theory has been adopted to avoid any potential shortcomings. [PERSON] et al. established the fault tree analysis method combined with quantitative analysis, in which the failure probabilities of basic events are assumed to be intuitionistic trapezoidal fuzzy numbers due to the incompleteness of the prior information and the complexity of the decision environment [9]. [PERSON] et al. proposed a method for the quantification and evaluation of fault trees in fuzzy environments [10]. It was designed for rare events and the identification of the probability and the underlying root cause of derailment. Ref.