Analysis Of Urban Bus Routes In Tegucigalpa, Honduras Through Operational Research
Bibliographic record
Abstract
The present research aimed to develop a route selection model using the knowledge and tools from the operational research field by applying the principles of linear programming.The goal arose from the need to update urban bus routes and their inefficient cycle times.It was necessary to establish a model that would adjust constraints and criteria so that it could later be applied in other companies in the urban bus sector.To achieve this, it began with an in-depth literature review across various databases containing articles and scientific journals, which subsequently formed the theoretical framework.Additionally, a review and verification of the most prominent articles were carried out, becoming a state-of-the-art analysis, where initial evaluation criteria were determined and also provided support for the selected analysis and resolution methods.As a result, it was determined that interviews with drivers and administrators would provide more accurate information to define the constraints and key variables for the linear programming model.Once the baseline data was obtained to develop the respective model, the creation and exemplification of it in an Excel spreadsheet template were carried out, followed by the use of Excel Solver software to verify its functionality according to the case of both analysed companies.Additionally, POM QM software was used to explore other possible solution methods for the bus routes of the analysed companies.Subsequently, validation was performed with experts in the field of operational research, thus demonstrating its applicability through consistent results closely resembling to the real data.Through these results, the calculations obtained in Excel and POM QM were validated.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".