Multimodal iNtelligent Deep (MiND) Traffic Signal Controller
Bibliographic record
Abstract
Population growth around the world has led to a challenging level of demand for transportation. Constructing new infrastructure is not always the first option due to spatial, financial, and environmental constrains. Public transit is often considered to be a more affordable and sustainable option, as one transit vehicle can carry significantly higher number of passengers compared to regular vehicles. In urban cores, a considerable portion of travel time is spent waiting at traffic signals. Transit Signal Priority (TSP) methods has emerged over the years to reduce transit delays at traffic signals. Traffic signals are often optimized for regular traffic and TSP systems are added to adjust the background signal timing plans to provide priority for transit vehicles. Therefore, these two modes seem to constantly fight for the green signal, and improving one's travel time leads to deterioration of the other's. In this research we introduce a new multimodal traffic signal controller that explicitly considers both regular and transit vehicles and optimizes the throughput of people rather than vehicles, irrespective of what mode they are on. For this purpose, we use deep reinforcement learning to develop and test a Multimodal iNtelligent Deep (MiND) traffic signal controller.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".