Evolutionary game theoretical approach for equilibrium of cross-border traffic
Bibliographic record
Abstract
This paper offers a game-theoretical approach and analysis for the traffic assignment problem over a set of checkpoints. In the provided formulation, the vehicle drivers’ are considered to be the rational players of the game whereas the selection among the different available checkpoints are their strategies to choose from. The objective of the game is to find an equilibrium distribution of vehicles in order to moderate the delay time at cross borders. An ad-hoc discrete event simulation is developed to show the validity of the overall approach and the findings reached. The results of the conducted simulation runs reaffirm the conclusions made showing that the system reaches stability. A discussion of the practical considerations and implementation details of the changes advocated at border-crossing points is included.Highlights A game-theoretical analysis is provided for the traffic assignment problem at border-crossings, where vehicle drivers’ are considered to be the rational players of the game and their strategies are the checkpoints they choose from. It is proven with the aid of Brouwer Fixed Point Theorem and Replicator Dynamics that should the vehicles be supplied with meaningful data about the quality of the different available checkpoints, equilibrium and evolutionary stable state (ESS) are reached.An ad-hoc algorithmic discrete event simulator is developed to demonstrate the validity of the game-theoretic analysis and the findings made.The developed approach can be easily put to practice should the proper sensory devices be installed at each checkpoint providing accurate meaningful indicators about their quality via radio frequencies or over cellular data infrastructures.This system could come to fruition if applied practically to a border-crossing point installing sensory devices and channeling input and feedback to vehicles across radio frequencies when the overall queuing system and vehicle distribution deviate from stability having a checkpoint shut down abruptly
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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 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".