Data-driven models for predicting delay recovery in high-speed rail
Why this work is in the frame
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Bibliographic record
Abstract
One of the main challenges arising in a high-speed railway (HSR) is predicting how fast a train, once delayed, can recover its operation. Accurate prediction of delay recovery in the downstream stations of a HSR line can help train dispatchers make adjustments to the timetables and inform the passengers of the expected delay to improve service reliability and increase passenger satisfaction. In this paper, we present the results of an effort to develop data-driven delay recovery prediction models using train operation records from the Centralized Traffic Control system (CTC) of Wuhan-Guangzhou (W-G) HSR in Guangzhou Railway Bureau. We first identified the main variables that contribute to delay, including total dwell (TD) time, running buffer (RB) time, magnitude of primary delay (PD), and individual sections' influence. Two alternative models, namely, multiple linear regression (MLR) and random forest regression (RFR), are calibrated and evaluated. The validation results on test datasets indicate that both models have good performance, with the RFR model outperforming the MLR in terms of prediction accuracy. Specifically, the evaluation results show that when the prediction tolerance is less than 3 minutes, the RFR model can achieve up to 90.9% of prediction accuracy, while this value is 84.4% for MLR model.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it