Transportation management associations: exploring public-private partnerships to enhance travel behaviour change programs
Bibliographic record
Abstract
Throughout the U.S., Canada, and Europe, the development of public-private partnership organisations has promoted enhanced private-sector involvement in transportation programs. Groups called Transportation Management Associations (TMAs) are involved in transportation issues in many different ways. TMAs emerged in the US in the early 1980s as public-private partnership organisations established to design and implement collaborative transportation management strategies addressing traffic congestion, mobility, and/or air quality problems in specific geographic areas. Today, approximately 150 TMAs are in operation, primarily in the US and Canada. Recently, start-up TMAs are also in the development stages in Great Britain (Dyce Area, Scotland) and New Zealand (North Harbour Industrial Area, North Shore City). This paper is intended to provide basic background information on the TMA experience in North America, and to present the lessons learned on TMA strengths and weaknesses from the author’s experience working with TMAs in a wide array of settings throughout North America. (a) For the covering entry of this conference, please see ITRD abstract no. E214666.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".