Development of an Intelligent System for the Recommendation of the Most Suitable Routes for Pedestrians
Bibliographic record
Abstract
Abstract. This study introduces an innovative approach to urban mobility through the development of an intelligent platform for smart cities. The platform leverages social media engagement to gather citizen insights about urban challenges, particularly focusing on pedestrian mobility and safety in District 6 of Tehran municipality. By encouraging users to discuss city problems, the system will collect valuable data on user preferences and experiences. The platform will utilize this crowdsourced information to provide personalized routing services that reflect the community's needs and concerns. Since the platform currently does not have active users, so a structured questionnaire was distributed to 100 participants to assess factors such as perceived safety, lighting conditions, and sidewalk width. This data was then integrated into a web-based public participation system, emphasizing the crucial role of citizen input in urban planning. Routing algorithms, including A* and Dijkstra's algorithms, were employed to identify optimized pedestrian routes based on the community feedback. Preliminary results suggest that these community-informed routes outperform conventional navigation systems, such as Google Maps, in addressing local conditions and user preferences. The platform not only enhances pedestrian experiences by prioritizing safety and accessibility but also demonstrates the potential of active citizen engagement in shaping urban environments. This approach represents a significant step towards creating more responsive and user-friendly smart cities, where citizen input directly influences urban services and planning decisions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".