Using Delay Logs and Machine Learning to Support Passenger Railway Operations
Bibliographic record
Abstract
Passenger railways face reliability challenges resulting from shared track with other trains, limited infrastructure capacity, and rolling stock and trackway that is subject to major failures during service. Dispatchers may have limited contextual information when responding to an emerging delay, and often rely on their own experience to manage an incident. This study leverages various aspects of delay logs—a common set of data collected during railway operations—to arm dispatchers with an understanding of delays, provide contextual information about previous delays that are similar to an emerging event, and make predictions about the size of a delay based on emerging information. Using graph theory, short-text topic modeling, cosine similarity, and machine learning regression models, we demonstrate that agencies can leverage this single data source for insight and operational support. To showcase the potential insights gained by these methods, we apply them to delay log data from the GO Rail network in the Greater Golden Horseshoe area of Ontario, Canada. We find that elastic net and random forest regression models outperform naive models that may be tacitly used in practice today.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".