Modeling Dwell Time for Streetcars in Melbourne, Australia, and Toronto, Canada
Bibliographic record
Abstract
Previous research indicates that dwell time is a major factor influencing transit competitiveness. Streetcars have particularly uncompetitive running times, but no research has explored influences on streetcar dwell time. There is also no analytical research on dwell time effects of stop design despite anecdotal evidence showing that platform stops have reduced streetcar dwell time. This paper presents an empirical study of factors affecting dwell time on streetcars in Melbourne, Australia, and Toronto, Canada. It focuses on tram stop design. Results show that payment of fares to drivers on entry in Toronto increases dwell time compared with onboard self-ticket validation in Melbourne (β = .26). For a typical case of 10 passengers boarding and five alighting, the Melbourne approach saves 9.4 s (48%) of dwell time compared with Toronto. Tram stop design, notably platform stops, was the next most significant factor affecting streetcar dwell time (β = -.18). For a typical case of 10 passengers boarding and five alighting, platform stops reduce dwell time by 6.6 s or 25%. A positive link between the number of doors on trams and dwell time was found; however, this is thought to result from insufficient examples of high boarding numbers on four-door trams. The results suggest that off-vehicle or postboarding ticket purchase and validation are significant strategies for reducing dwell time. Providing platform stops is also a potential strategy for reducing dwell time. Areas for future research are suggested.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".