Performance Measurement of Transportation Systems: Summary of the Fourth International Conference
Bibliographic record
Abstract
This report was prepared by the conference rapporteur as a factual summary of what occurred at a recent conference on U.S. and international approaches to performance measurement for transportation systems. The theme for the fourth in a series of international conferences, driving change and being driven by change, captured the changing environment in which transportation services are delivered as well as the role of performance measurement in delivering these services. The conference attracted 130 participants from Canada, Germany, Japan, the Netherlands, South Africa, and the United States, and featured transportation specialists who offered real-world expertise on the application of performance metrics and case studies. This range of experiences provided attendees with a comprehensive overview of the techniques and approaches being applied to transportation systems both in the United States and abroad. The conference program was organized and developed along five tracks: 1) driving forces for change; 2) performance-based decision making: the bucks start here; 3) data collection and analysis technologies; 4) drivers and applications; and 5) capturing system performance: new measures for difficult-to-measure topics. Each track consisted of a plenary session followed by three concurrent breakout sessions. These proceedings follow the conference format, with the plenary sessions and the breakout sessions for each of the five tracks presented in chronological order. The breakout sessions and the closing session gave participants the opportunity to provide ideas and suggestions on further research, technology transfer, and training. Research topics identified for potential consideration are listed in the section on concluding remarks. The conference also featured an interactive poster session. Summaries provided by the poster authors are presented as an appendix.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.013 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".