Evaluation of Road Infrastructure Alternatives Through a Saving Gradient in Travel Times. Case Study: Second Connection Villamaría-Manizales, Colombia
Bibliographic record
Abstract
In the last century, the migration of people from rural to urban areas of cities has generated a set of dynamics in different sectors such as social, economic, educational that have led cities to collaborate among them, generating a constant synergy in order to obtain a sustained development in multiple aspects. In this sense, Manizales as the capital of the department of Caldas in Colombia has generated a conurbation with the nearest neighboring municipality, Villamaría reaching a combined population of 419 943. Although this synergy has taken place for several decades, these municipalities only have a place of connection, because they are separated by a geographical barrier, the Chinchiná River. This connection has had clear connection problems, due to the high flow of vehicles that move between both municipalities, so it is essential to project a second connection, to further boost mobility among its inhabitants. For this reason, in this research four (4) alternatives of connection are proposed, according to the suggestions of the Manizales 2017 Mobility Master Plan, through the calculation of the global average accessibility and the quantification of the gradient of savings generated in the average times of trip, based on the current situation of both cities. The results show which is the alternative that benefits population the most in terms of travel time savings, although three of them generate considerable savings and only one is discarded because it benefits a smaller number of inhabitants in very low percentages of savings compared to the others. The evaluation of infrastructure alternatives through the gradient of savings in travel times is useful to determine the best options in the pre-feasibility phase of investment projects in the transport area.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".