Bit by bit: A method for using bus data to develop plan bus priority interventions in Portland, Oregon, USA
Bibliographic record
Abstract
Bus Priority Interventions are small-scale changes that improve bus speed and reliability. These include changes to street geometry, bus stops, and traffic signals. Automated Vehicle Location-Automated Passenger Counting (AVL-APC) data can help transit agencies by providing insight into bus location, speed, and passenger volumes. This work proposes an end-to-end methodology for using AVL-APC data to create a concept design for bus priority interventions on a bus corridor. The proposed method is illustrated by analyzing a bus route in Portland, Oregon. This mixed-methods approach paired quantitative data analysis with site visits to identify what was causing delay on the route and suggest targeted interventions. Scenario analysis of historical trip data was employed to predict the impact of different interventions. Historical trips that fell into two different scenarios were compared: a delay scenario (where a specific delay-inducing event occurred, like a red light) and a non-delay scenario (where that event did not occur). This end-to-end methodology could be used by transit agencies and transportation planners to quickly assess different corridors and interventions, diagnose problems, and determine which projects would create the greatest customer and financial benefits. Employing this approach could help planners prioritize time and resources to ensure that the highest impact projects are pursued.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".