Comparative analysis of sustainable mobility planning tools: case studies from Italy, Egypt, and Lebanon
Bibliographic record
Abstract
This paper investigates the integration between different scales of sustainable mobility planning, with particular reference to the integration between Sustainable Urban Mobility Planning (SUMP) and Urban Design. It was carried out in the framework of the EU Erasmus+ UPGRADE project – Urban and transPortation reGeneration for Reducing Automobile Dependency in Middle East and North Africa – MENA area, whose cities are facing major challenges to improve their sustainable mobility. The study uses a comparative method to compare planning models and tools in the European Union, currently limited to the Italian case, with those in the MENA region currently limited to Egypt and Lebanon. On the one hand, the qualitative analysis concerns the integration between sustainable mobility planning and regional and urban spatial planning, looking for relationships (horizontal and vertical coherence checks) and criticalities between them. On the other hand, the analysis concerns the integration of the above levels and types of planning with urban design practices aimed at sustainable mobility. The application of the methodology revealed some criticalities inherent in the processes differentiated according to the countries considered, highlighting key disconnections between SUMP and Urban Mobility Design practices. The next steps of the research will be to expand the case studies and search for good practices in urban mobility design.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".