Measuring the operational impacts of a new Bus Rapid Transit (BRT) in Montreal, Canada
Bibliographic record
Abstract
Recent research on Bus Rapid Transit (BRT) systems has mostly focused on ridership forecasting and scheduled travel time gains, with little empirical evidence on potential operational improvements. This study examines the short-term impacts of implementing a new BRT corridor in Montreal, Canada, on key bus performance indicators: running time, running time deviation, and headway deviation. Using Automatic Vehicle Location (AVL) and Automated Passenger Count (APC) data from 2022 to 2023, we compare the performance of the BRT to a parallel local bus route operating along the same corridor, before and after the BRT implementation. Our findings indicate that the BRT significantly reduced trip durations (about four minutes on average) primarily due to infrastructure features such as dedicated lanes and all-door boarding policy. The local route experienced modest running time improvements post-BRT, suggesting potential corridor-wide benefits. However, run time deviation was significantly higher for the BRT, particularly during peak periods while headway deviation worsened along the corridor compared to pre-BRT conditions. These findings highlight the importance of integrating infrastructure investments with dynamic operational strategies such as real-time dispatching and headway control. It emphasizes the need for schedule calibration following implementation to ensure that planned service aligns with actual performance. These findings offer practical insights for transit agencies planning or managing BRT systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".