Reaching destination on time with cooperative intelligent transportation systems
Bibliographic record
Abstract
Summary Research on intelligent transportation systems is so far focussed on decreasing the travel time of vehicles and avoiding congestions. However, the importance of reaching on time is different for different vehicles and depends upon the purpose of the journey. In a human‐operated queue, it is generally considered courteous to give priority to people running very late. They may be running late to catch a flight or may be in an emergency for a medical check‐up. There is a very small discomfort to the other people as long as the number of people in an emergency and running late is low. However, such prioritization is an invaluable help to the people running late. In this paper the same behaviour is modelled, wherein the transportation system is made biased towards the vehicles on an important task and running late. The paper presents the mechanism by which a vehicle may judge its running status, decide whether to ask for cooperation and decide how much of cooperation to ask for. The vehicle lane changes and traffic lights operating policy are made cooperative. Experimental results show that such a cooperation leads to lesser number of important vehicles reaching their destinations late. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".