Variation in Bus Transit Service: Understanding the Impacts of Various Improvement Strategies on Transit Service Reliability
Bibliographic record
Abstract
Transit agencies wishing to offer reliable service with less variability face several challenges, encouraging them to employ various strategies. While previous research has considered the effects of various strategies on running time, there has been little effort to understand their impacts on reliability of service. This article examines the impacts of various improvement strategies on running time deviation from schedule, variation in running time, and variation in running time deviation from schedules. These strategies include implementation of a smart card fare collection system, operation of a reserved bus lane, introduction of limited-stop bus service, use of articulated buses, and operation of transit signal priority (TSP). This study conduct this examination using data obtained from the Societe de Transport de Montreal (STM)’s automatic vehicle location (AVL) and automatic passenger count (APC) systems, in Montreal, Canada, at the bus route segment level of analysis. The introduction of a smart card fare collection system increased bus running time and service variation. Articulated buses, limited-stop bus service and reserved bus lanes have mixed effects on variation in comparison to the running time changes, while TSP did not show an impact on variations in our study. This study offers transit agencies and schedulers a better understanding of the effects of various strategies on different aspects of service variation, which are important components of transit service reliability.
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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 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".