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Record W2887742868 · doi:10.5539/mas.v12n9p28

The Public Space as an Articulating and Structuring Element for the Sustainable Urban Mobility. Case Study: Parking management in Villavicencio, Colombia

2018· article· en· W2887742868 on OpenAlexvenueno aff
Carlos A. Moncada, Ximena Carolina Velandia

Bibliographic record

VenueModern Applied Science · 2018
Typearticle
Languageen
FieldEngineering
TopicSmart Parking Systems Research
Canadian institutionsnot available
FundersUniversidad Nacional de ColombiaTechnische Universität Kaiserslautern
KeywordsParking spaceTransport engineeringBusinessPublic transportSustainabilitySustainable transportOccupancyEnvironmental planningGeographyCivil engineeringEngineering

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.292
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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