Distributed Perimeter Flow Control of Transport Networks
Bibliographic record
Abstract
In this paper, we develop a distributed control scheme for the perimeter traffic flow control problem in urban road networks. The proposed scheme determines optimally distributed input flows for a number of gates located at the periphery of a protected network area. A parsimo- nious model is employed to describe the traffic dynamics of the protected network. To describe traffic dynamics outside of the protected area, we augment the basic state-space model with additional state variables for the queues at store-and-forward origin links at the periphery. We aim to equalise the relative queues at origin links and maintain the vehicle accumulation in the protected network around a desired point, while the system’s throughput is maximised. The perimeter traffic flow control problem is formulated as a convex optimal control problem with constrained control and state variables. Simulation results are carried for a protected area of downtown San Francisco with fifteen gates of different geometric characteristics. Results demonstrate the efficiency and equity properties of the proposed approach to better manage excessive queues outside of the protected area and optimally distribute the input flows.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".