Celer: A Smart Fleet Management System (Optimizing Traffic Flow in New York City)
Bibliographic record
Abstract
As society moves closer to fully autonomous vehicles, it must eventually make vehicles work together. This would reduce traffic jams, reduce cost of trips, reduce overall travel time, reduce the environmental impact, and reduce the number of casualties to traffic. [1] However, society's focus has mostly gone towards making the vehicles autonomous and not towards making a system that would manage a set of robo-taxis. This gap in research should be thoroughly explored because although autonomous vehicles are safer, they are not necessarily more efficient in reducing traffic jams and the cost of trips. [6] There have been many promising studies in tackling individual issues that such a system would face. These include finding an efficient route from point A to point B [2, 3], optimizing intersections [4], tackling road hazards [6], and more. By combining many preexisting algorithms into one system, Celer attempts to optimize traffic flow in New York City and explore the problem of car interconnectivity. Celer is able to reconstruct a map of New York City and uses taxi data from 2015 to simulate real world conditions. Overall, Celer improved trip time and profits substantially and showed a promising solution to the fleet management problem.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".