Estimating intersection delays to transit vehicles using archived AVL/APC data
Bibliographic record
Abstract
Many transit agencies have deployed automatic vehicle location (AVL) systems and/or automatic passenger counting (APC) systems on a portion of their fleet of transit vehicles. Data from these systems are used for real-time system monitoring and control and archived data are often used for service performance reporting and for service planning. This paper proposes and demonstrates a method by which these archived data can be used to estimate the mean and variance of transit vehicle delays caused by signalized and unsignalized intersections as well as the horizontal extent of the queue. These quantitative measures are of particular value for identifying intersections at which transit priority measures would be most useful. The proposed method is suitable for application to most transit AVL/APC databases and is demonstrated using data from Grand River Transit, the public transit service provider in the Region of Waterloo, Ontario Canada. These results suggest that the proposed method has practical application for the identification and prioritization of candidate transit priority measures, including transit signal priority (green extension, early green, special transit phases) and queue jump lanes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".