Estimating Signalized Intersection Delays to Transit Vehicles
Bibliographic record
Abstract
Many transit agencies have deployed automatic vehicle location (AVL) systems, automatic passenger counting (APC) systems, or both on a portion of their transit vehicle fleets. Data from these systems are used for realtime 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 intersections. The proposed method is suitable for application to most transit AVL-APC databases and is demonstrated with data from Grand River Transit, the public transit service provider in the region of Waterloo, Ontario, Canada. The results obtained from the application to field data indicated that the proposed method was able to explain 96% of the variation in observed mean transit vehicle delay at signalized intersections. These results suggest that the proposed method has practical application for the identification and prioritization of candidate measures for transit priority, 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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".