“Tram Wrong Way” International Experience and Mitigation of Track Switch Errors
Bibliographic record
Abstract
Trams can go the wrong way at track switches causing disruption and safety risks. In 2019 Melbourne experienced 370 incidents, Toronto 155, and Zurich 194. This paper explores wrong-way incident occurrences and mitigations. It explores literature, reviews global practice, and mines an incident database in Melbourne. Driver fatigue is the principal cause of rail crashes in Australia. Cognitive secondary tasks such as point and call (PAC) can focus vigilance, but no published evidence on PAC effectiveness on railways was found beyond laboratory experiments. However, this study found evidence of PAC effectiveness in San Diego and Toronto, with a broadly 30% to 40% incident reduction impact. International practice shows widespread occurrence of incidents. Automated switch control systems and the removal of driver in-cab control of points can reduce incident occurrence, but not entirely. San Diego has in-cab control but low incident rates, which result from PAC-type measures. A review of Melbourne incidents found that January/February and November are high incident months and Monday/Friday high incident days: each have special event working, and on Monday drivers may be starting a new week of shifts. During the day, early prepeak services, 10 a.m., and evening peaks had high incident occurrences, potentially associated with shift start/changing patterns. Incidents were highly concentrated: 42% at only 10% of switches. New drivers had high incident rates: 15% of incidents in Melbourne, 30% in Toronto, and 33% in Zurich involved drivers in their first year. Findings on incident mitigation and directions for future research are identified.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".