Train operation conflict detection for high-speed railways: a naïve Bayes approach
Bibliographic record
Abstract
Accurately detecting train operation conflicts (TOC) has great significance for improving the emergency handling ability of dispatchers during interference. In this study, a conflict detection model for high-speed train operation is proposed, with the train operation data from Xiamen to Shenzhen high-speed railway. Firstly, a TOC detection model framework considering data imbalance is determined, based on Bernoulli naïve Bayes model. Then, the hyper-parameter of the proposed model is tuned with the training and validation dataset. Next, the performance result of the proposed model is compared to other three commonly used naïve Bayes models, namely the Gaussian naïve Bayes, multinomial naïve Bayes and complement naïve Bayes. Comparison analyses based on the commonly used classification model evaluation indexes show that the detection accuracy of the proposed model is significantly higher than other naïve Bayes models. The proposed model also achieves high robustness and detection accuracy in each category.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".