Does Roadway Performance Affect Transit Headway Unreliability? Evidence from Mixed-Traffic Transit Corridors in Toronto, Canada
Bibliographic record
Abstract
This research assesses the links between roadway performance and transit headway unreliability. Frequent and reliable transit services are valued by transit users and feature as critical components affecting generalized travel costs. But retaining high quality and reliable transit services in mixed-traffic urban contexts is challenging because more and more users are competing for limited physical road space, making an understanding of service unreliability critical for effective transportation system management in growing urban contexts. This study focuses transit headway unreliability on three major downtown Toronto arterial transit corridors—King Street, Queen Street, and Dundas Street. These corridors are primarily served by streetcars but are also occasionally served by buses in cases of diversions or street closures. This research uses Inrix, Inc. probe data to measure traffic congestion and roadway service unreliability and to explore its links with transit headway reliability. Both inferential models and descriptive statistics are presented to identify patterns in transit headway reliability and estimate the strength of association with traffic congestion. Results indicate that roadway performance levels and terminus departure headway unreliability are linked with stop-level transit headway unreliability. Nevertheless, much of the variation in transit headway unreliability remains unexplained by inferential models. Results indicate that insofar as traffic management actions can affect congestion levels, this may improve transit headway reliability. However, one should temper expectations of congestion management affecting mixed-traffic transit services, particularly in a highly urbanized, mixed-traffic environment such as downtown Toronto. Instead, findings suggest meaningful improvements in stop-level headway reliability from better initial scheduling and headways at the beginning of these transit routes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".