Bibliographic record
Abstract
A “road diet” consists of converting a four-lane undivided roadway to a two-lane roadway plus two-way left turn lane by removing a travel lane in each direction. The remaining roadway width can be converted to bike lanes, on-street parking or sidewalks. In cities throughout the world, roadways have been put on “road diets,” generating benefits to all modes of transportation including transit, bicyclists, pedestrians and motorists. These benefits include reduced vehicle speeds, improved mobility and access, reduced collisions and injuries, and improved livability and quality of life. This paper will explore and evaluate the livability impacts of road diet projects. These livability impacts have not been previously evaluated in any research effort or manner. The impacts of the road diet projects evaluated include improved quality of life, street character, and comfort and safety for pedestrians, bicycles, and transit. The content, application, and results of a public opinion livability survey will be presented. The survey was administered along four-lane undivided and three-lane streets with comparable width, character, and traffic flow. The livability survey solicited information from people living and working adjacent to the streets with factors directly related to its livability. Five sites were chosen for the survey and data collection in Washington, Iowa, and Georgia, and in Canada and New Zealand. The focus of the paper will be on the impacts of road diet projects on livability and context sensitivity.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".