A Cultural Route Recommendation Based on Optimization Techniques in Urban Spaces
Bibliographic record
Abstract
This study explores the increasing global interest in the preservation and management of cultural heritage, emphasizing the need for innovative tools in urban planning and design to address this critical issue.As cultural heritage gains international attention, particularly through the concept of cultural routes, there is a growing demand for urban planning strategies that are both flexible and forward-thinking.The shift from traditional planning approaches to advanced digital systems, particularly those driven by artificial intelligence, marks a significant transformation in the field.This study aims to introduce an innovative application recommendation, designed to optimize urban environments by leveraging the dynamic potential of cultural routes.Central to this approach is the Ant Colony Optimization (ACO) technique, enhanced with the 2-Opt algorithm based on the Traveling Salesman Problem (TSP), a highly effective method widely used for solving complex routing problems.By integrating this improved ACO, the recommendation not only generates optimal routes tailored to user preferences but also enhances efficiency in both time and budget management.Beyond its technical merits, this optimization-based solution offers a holistic approach to urban planning by integrating cultural heritage management with contemporary technological advancements.By doing so, the recommendation is expected to contribute to the sustainable development of cities, ensuring that cultural heritage is preserved while also addressing the practical needs of urban environments.This research presents a forward-thinking proposal that aligns with global trends in cultural preservation, offering a functional and adaptable tool for the challenges faced by the user.The findings of this study show that the proposed cultural route planning and design model can create specific routes according to individual interests, thus helping to personalize travel experiences and determine the most appropriate routes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".