A Solution to the Congestion Problem: Profiles Driven Trip Planning
Bibliographic record
Abstract
Modern transportation systems suffer from a variety of problems. The most notable among these problems is traffic congestions. Traffic congestions have been a subject for much of research discussing the impact of the traffic on the environment, the financial cost it incurs, and the safety risks it poses. The research of this paper alternatively suggests a solution model to mitigate the congestion problem: the personalization of the trip planning process based on the social habits of the travellers. These habits can be derived from monitoring daily activities, calendar events, and social feeds from the various outlets of social media. The different sentiments of the travellers can be processed to generate several planning profiles. Travellers will be able to choose the profiles representing their personalities and subject them to further customization. The customized profile can be considered as an avatar. As the avatar assumes the traveller's planning persona, it will start to automatically plan the daily trips on behalf of the traveller. To fully grasp the functionality of the proposed approach, Game theory is used to analyze the new trip planning approach and its effect on the traffic flow.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".