Energy Savings Strategies for Transit Agencies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This synthesis describes how transit agencies in the United States and Canada are reducing their energy use. This is being done not only by providing alternatives to travel in personal vehicles but also in other categories of energy savings strategies such as those dealing with vehicle technologies; vehicle operations, maintenance, and service design; non-revenue vehicles; stations and stops; building; indirect energy use; and renewable power generation. These strategies can reduce both an agency’s costs and its’ environmental footprint, and some can also improve service quality. A review of the relevant literature of a variety of academic and professional publications was conducted for this effort. A selected survey of 51 respondents out of 74 transportation providers located in large metro, small urban, and rural areas yielded a 69% response rate. Four transit providers highlighted more in-depth and additional details on successful practices, challenges, and lessons learned: Southeastern Pennsylvania Transportation Authority, Philadelphia, Pennsylvania; King County Metro Transit, Seattle, Washington; Foothill Transit, West Covina, California; and 9 Town Transit, Connecticut River Estuary, Connecticut.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it