Sustainable Complete Streets Design Criteria and Case Study in Naples, Italy
Bibliographic record
Abstract
Background: A growing number of communities are re-discovering the value of their streets as important public spaces for many aspects of daily life, creating the need for a transformation in the quality of those streets. An emerging concept of ‘complete streets’ is to accommodate all users of the transportation system. Methods: In this paper, we present sustainable complete streets design criteria that integrate complete streets by adding socio-environmental design criteria related to the aesthetics, environment, liveability, and safety. To help set priorities, identify the street design features, and create intuitive multimodal networks throughout the city, we have defined a list of the general and specific criteria to be addressed for sustainable complete streets. Results: The proposed design criteria provide a street network with improvements in its aesthetics, to recover the historical urban character and realize historical area planning goals; the environment, to increase the permeable surfaces, reduce the heat island effect, and to absorb traffic-related air pollution; the liveability, to create a public space destination in the urban landscape; and safety, to improve the safety of all road users. The design scenarios proposed in the study were conceived to help practitioners to consider these context-based uses and design accordingly by gaining knowledge from past experiences to benefit future projects. Conclusions: The case study of the urban rehabilitation of the “Mostra d’Oltremare” area and its cultural and architectural assets in Naples, Italy, highlights the practical application of the proposed criteria and the possibility of using these criteria in other urban contexts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".