Beyond Generating Transit Performance Measures
Bibliographic record
Abstract
In recent years, the use of performance measures for transit planning and operations has gained a great deal of attention, particularly as transit agencies are required to provide service under increasing demand and with diminishing resources. The widespread application of the technologies of intelligent transportation systems to transit encourages automating the generation of comprehensive performance measures. In Portland, Oregon, the local transit provider, Tri-County Metropolitan Transportation District of Oregon (TriMet), has been on the leading edge of the transit industry since it implemented its bus dispatch system (BDS) in 1997. The BDS comprises automatic vehicle location on all buses, a radio communications system, automatic passenger counters on most vehicles, and a central dispatch center. Most significant, TriMet developed a system to archive all its stop-level data, which are then available for conversion to performance indicators. In the past decade, TriMet has used this system extensively to generate performance indicators through monthly, quarterly, and annual reporting. TriMet generates a wide range of performance indicators, yet an opportunity remains to explore metrics beyond general transit performance measures (TPMs). On the basis of an analysis of 1 year of archived BDS data for all routes and stops, the power of using visualization tools to understand the abundance of BDS data is demonstrated. In addition, several statistical models are generated to demonstrate the power of statistical analysis in conveying valuable and new TPMs beyond what is currently generated at TriMet or in the transit industry in general. It is envisioned that systematic use of these new methods and TPMs can help TriMet and other transit agencies improve the quality and reliability of their service.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".