BNART: A Novel Centralized Traffic Management Approach for Autonomous Vehicles
Bibliographic record
Abstract
Traffic management poses a critical challenge in modern metropolitan areas, where efficient management can significantly reduce travel time and vehicle emissions. With the vision of smart cities in mind, the increasing prominence of autonomous cars is anticipated to revolutionize road networks under the control of a central supercomputer known as the Central Traffic Control Unit, which maintains real-time location information for each vehicle within its jurisdiction and assumes responsibility for path planning. This paper investigates the problem of traffic management for self-driving vehicles, focusing on the utilization of essential information, such as source and destination locations, to guide vehicles along the most efficient routes, mitigating road congestion and ensuring prompt arrivals. To address this challenge, we formulate the problem as a mixed-integer linear programming model. Furthermore, due to its complexity, we propose a heuristic method called the Best Neighbor Algorithm for Routing Traffic (BNART). We extensively evaluate the performance of our proposed heuristic approach against optimal solutions on small-scale instances and other state-of-the-art methods such as the shortest path algorithm on large-scale instances. Through simulation, we demonstrate that our proposed method outperforms other methods in terms of average travel time, exhibiting a negligible gap to the optimal solution.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".