Subway Service Down Again? Assessing the Effects of Subway Service Interruptions on Local Surface Transit Performance
Bibliographic record
Abstract
Cities around the world are keen to offer modern urban transit systems that connect various locations in an efficient and reliable manner with the highest degrees of riders’ experience. These systems may consist of various modes such as buses, streetcars (or trams), and rapid transit systems (e.g., subways). In both research and practice, the quality of transit service has traditionally been measured and investigated on a mode-by-mode basis. Therefore, it is rare to find studies that investigate the impacts of poor performance or breakdown of one transit mode on other functioning modes in multimodal integrated transit systems. This research aims at understanding the impact of subway service interruptions on the speed performance of surface transit in Toronto, Ontario. To do that, a detailed data set of subway service interruptions collected in 2013 by the Toronto Transit Commission (TTC), the public transit provider in the City of Toronto, was used. In addition, another data set was obtained from the TTC’s automatic vehicle location system for 51 bus and streetcar routes that are within a short walking distance of some subway stations. Using two statistical models, the paper results indicate that subway service interruptions have a statistically significant negative impact on bus and streetcar service operations in terms of slower speeds, with more immediate and intense impacts on streetcar service.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".