The Public Space as an Articulating and Structuring Element for the Sustainable Urban Mobility. Case Study: Parking management in Villavicencio, Colombia
Bibliographic record
Abstract
With the purpose of contributing to knowledge in parking management, this research based on sustainable mobility and the right to enjoy public space, aims to generate guidelines that promote an urban lifestyle centered on integration between different modes of transport, motorized and non-motorized. In this sense, a case study is included on parking management in the city of Villavicencio (department of Meta), in which an inventory and characterization of on-street and off-street car parks is presented as the main input. Based on this information, a methodology is proposed, aimed at guaranteeing off-street parking with capacities greater than 50 spaces, eliminating those with low capacity and relocating their demand in parking lots with higher capacities and low occupancy percentages. This will allow financial sustainability so that off-street parking can offer adequate security, infrastructure and fare conditions that make them more attractive compared to on-street parking areas. Additionally, it is proposed the elimination of parking on the road with access or exit to the arterial road and the elimination of areas for this type of parking that intersect with public transport routes or infrastructure for bicyclists.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".