Dynamic Railway Derailment Risk Analysis with Text-Data-Based Bayesian Network
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".