Optimizing road intersection traffic flow using stochastic and heuristic algorithms
Bibliographic record
Abstract
Vehicular Ad-hoc Network, VANET, offers new opportunities for better road traffic management. Using vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications, several research works were published on road traffic improvement in general and at road intersection in particular. One of the challenging problems is to minimize vehicles' waiting time at the intersection as well optimize traffic flow by adjusting the traffic signals timing. In this area, several methods have been proposed in the literature which can be classified as adaptive, heuristic, pre-timed and others. In these methods traffic density and statistical data are generally used to determine the traffic signals timing. However, these methods either don't offer an optimal solution or are not cost effective in terms of computational time. In this paper, we propose the use of Simultaneous Perturbation Stochastic Algorithm (SPSA) to compute in real time the best traffic signals timing that minimizes the vehicles' travel time at the road intersection. A specific intersection in the city of Moncton with real traffic data was studied. Simulation results show a substantial improvement in term of vehicles' travel time in comparison with the pre-timed setting currently used by the city of Moncton. We also propose a heuristic algorithm which performs well too.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".