Transforming Public Transportation Institutional and Business Models
Bibliographic record
Abstract
This report provides an analysis of and strategy for defining and implementing transformative change in institutional and business models for operating and maintaining public transportation systems. It addresses a wide range of issues important to continued efficient operations, including responding to the needs of affected customer markets, improving operating procedures, implementing effective capital asset management, carrying out long-term planning and mission definition, improving and expanding functional areas as well as expertise and technical skill sets of the workforce, identifying and improving necessary business practices, identifying and implementing innovative funding mechanisms, implementing performance measures and improved governance models, identifying required organizational structures, and instituting effective area-wide collaborative practices. The report identifies the components of transformative change and develops a substantive typology to assist agencies in organizing and structuring an approach to defining and implementing components of productive change. The report further examines potential consequences of change that should be considered by agencies or organizations contemplating new institutional and business models. The analysis is based on a detailed case study approach that examines experience in 13 locations throughout the country and one in Canada, representing a diverse set of agencies with widely varying demographic and operational characteristics. These case studies are described in detail, and case study outcomes are used to help define what contributes to successful implementation. Each of the case studies describes background conditions and agency characteristics, type and nature of transformation undertaken, rationale for change, approaches to planning and infrastructure development, methods of community and agency collaboration and cooperation, outcomes and consequences of change, and lessons learned. The target audience for this study includes leaders who are seeking to transform public transportation organizations. Within transit agencies, the audience includes general managers, deputy general managers, and other senior leadership. Outside of transit agencies, the target audience includes political leaders, regional business leaders, leaders in partner transportation and planning agencies, and other key stakeholders that have the ability to influence the direction of public transportation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".