APOLO: A Mobility Pattern Analysis Approach to Improve Urban Mobility
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Urban mobility becomes one of the most challenging issues in large urban centers, since traffic congestion is a daily problem. In order to address this issue, a number of researchers, from both academia and industry, have studied several Traffic Management Systems (TMS) approaches to improve urban mobility. However, the existing approaches do not consider an essential factor: the population information. Within this context, this work proposes a new approach, called APOLO, that employs historical knowledge of mobility patterns of the drivers to obtain a global view of the road network. APOLO is different from others research approaches that need constant information exchange among the vehicles and the central server in order to obtain a global view of road traffic condition. These existing approaches can lead to network overload and have a high data processing cost in real-time. Results show that APOLO improves vehicles' mobility compared to well-known approaches, which indicates that APOLO could be a potential alternative for providing TMS services with valuable mobility knowledge.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it