Connected Vehicle-enabled Centralized Routing with Dynamic Network Allocation
Bibliographic record
Abstract
The Macroscopic Fundamental Diagram (MFD) enables effective and cost-efficient network traffic management to gain system optimum. The recent emergence of connected vehicles (CVs) opens avenues to leverage MFD for centralized management, further enhancing urban network mobility. This paper aims to augment an existing MFD-based centralized routing system with dynamic locality allocation. Employing the Girvan-Newman algorithm, the system divides the network's localities into smaller regions, each reflecting distinct traffic conditions, computing resource availability, and management efficiency. This approach enhances traffic management and optimizes resource utilization, ultimately improving system performance. Centralized routing, based on dynamic localities, seeks optimal CV paths, reducing computational costs and achieving network optimization. Evaluation results validate the centralized system's advantages in enhancing urban mobility across various CV Market Penetration Rates (MPRs) and evolving traffic conditions. The dynamic locality allocation system surpasses static locality counterparts, particularly ex-celling at high MPRs. Notably, in scenarios with very high MPRs, the centralized system slightly sacrifices CV mobility benefits to enhance overall network performance. Overall, this enhancement is essential in elevating network-wide vehicle performance, ensuring smoother traffic flow, and mitigating congestion levels.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".