Panel Regression Analysis of Disruption Frequency of Urban Rail Transit Systems
Bibliographic record
Abstract
High-frequency short delays in the urban rail transit system result in a cumulative delay effect within the network, which in turn affects the daily operational organization. Most studies focus more on long-term disruption, but there is less research on high-frequency short delays, lacking detailed classification and definition. Based on the 13-week operation data of Nanjing Metro and the detailed division of short-delay frequency, this study constructed four panel-regression models and compared and explained the influencing factors from multiple perspectives. The results show that the negative binomial fixed-effects model has the best goodness of fit. The two-way fixed-effects model increased complexity but does not improve fitting performance compared with the fixed-effects model. There are no significant time fixed effects in the three types of frequency data. New routes, remote stations with low passenger volume, stations with more train trips, and stations with surrounding land use of commercial service facilities have a higher frequency of minor delays, highlighting the need to deploy adequate facilities at these stations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".