Cyber-Physical Spatial Decision Support System for Road Traffic Management
Bibliographic record
Abstract
Nowadays, most of growing cities in the world are witnessing an unprecedented increase in road traffic congestion because of population mobility and sporadic events like accidents and natural disasters. As these congestions generally result in substantial casualties and economic losses, tremendous investments are being spent on efficient solutions for road traffic management. Abundant works have proposed solutions to help road traffic stakeholders in making decisions about ongoing events at the individual and collective levels. However, not enough success is yet achieved when it comes to collecting, processing, and delivering the right data, from the right location, at the right time to the right user. We argue in this paper that the divide should be effectively closed between a real world where road traffic and its related events are happening and a world where decisions are being taken. To this end, we propose to use the emergent technologies of Cyber Physical Systems along with multi-agent system mechanisms for additional autonomy, flexibility, and control of the different aspects of the highly dynamic and uncertain field of road traffic management. Within this scope, we propose an architecture of a Cyber Physical Spatial Decision Support System (CPSDSS) through which we explain how various road traffic challenges could be monitored and controlled
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".