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.
Highlights
Railway transportation has exhibited an upward trend in terms of the daily demand, especially because of its considerably lower price than air transportation and higher speed than shipping transportation
As our study focuses on text data, to further the research, we first apply the text method and establish fault trees based on the text records
The precision, recall and F-measure of text-based Bayesian network (TBN) are 8.64%, 10.70% and 9.84% higher than the most frequent (MF) result respectively, which shows that the performance of NWS is better
Summary
Railway transportation has exhibited an upward trend in terms of the daily demand, especially because of its considerably lower price than air transportation and higher speed than shipping transportation. Proposed a railway fault spreading model based on the dynamics of the process of failure interactions in networks [7]. The graphical model reveals the accident occurrence principle and process and shows the relations between barriers, which are the fundamental relations in BNs. Second, we perform text processing and characterize the text features and establish fault trees based on the text representation information. The analysis results represent the quantitative risk and reveal the key barriers and critical potential risks of defects; the proposed method has the advantage that the determined cause chains considering safety barriers better fit the accident characteristics than the cause chains directly determined from text records.
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