Measuring the operational impacts of a new Bus Rapid Transit (BRT) in Montreal, Canada
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it