Beyond Generating Transit Performance Measures: Visualizations and Statistical Analysis Using Historical Data
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 with increasing demand and diminishing resources.The widespread application of intelligent transportation systems (ITS) technologies in transit systems has opened the window for automating the generation of comprehensive performance measures.In Portland Oregon, the local transit provider (TriMet) has been on the leading edge of the transit industry since they implemented their bus dispatch system (BDS) in 1997.The BDS is comprised of automatic vehicle location (AVL) on all buses, a radio communications system, automatic passenger counters (APCs) on most vehicles and a central dispatch center.Most significantly, TriMet had the foresight to develop a system to archive all of its stop-level data that is then available for conversion to performance indicators.In the last decade TriMet has extensively used this system to generate performance indicators through monthly, quarterly, and annual reporting.TriMet currently generates a number of performance indicators, yet the road is still open to explore more opportunities beyond general transit performance measures.In particular, based on an analysis of one year of archived BDS data, this paper demonstrates the power of using visualization tools to understand the abundance of BDS data.In addition, several statistical models are generated to show the power of statistical analysis in conveying valuable and new transit performance measures (TPMs) beyond what is currently generated at TriMet or in the transit industry in general.It is envisioned that systematic use of these new visualization methods and TPMs can assist TriMet and any other transit agency in improving the quality and reliability of its service, leading to improvements to customers and operators alike.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".