Extraboard Team Sizing: An Analysis of Short Unscheduled Absences Among Regular Transit Operators, Case Study of OC Transpo, Ottawa, Canada
Bibliographic record
Abstract
Several factors contribute to short-duration unscheduled absences of bus transit operators (drivers). This article aims to understand these factors at the aggregate level and to anticipate future total absence that will need to be filled for a large-size transit operator. The aggregate level is defined as the total number of regular operator absences per garage, day of week and time period that need to be covered by the extraboards. This study analyzes absenteeism data obtained from OC Transpo, the transit provider of the city of Ottawa, Canada. A multilevel regression model is generated to investigate regular operators’ absence. The short-unscheduled absence is estimated in relation to temporal factors, operators’ personal characteristics, aspects of assigned work, and service delivery characteristics. Furthermore, using the model’s coefficients, sensitivity analyses are conducted to demonstrate the advantages of this technique over traditional ones being adopted by various transit agencies. This study provides transit planners and policy makers with a practical methodology that can be used to support extraboard planning practice and help reduce the incidence of missed trips due to absences while having the appropriate size of extraboard operators.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".