eMARLIN+: Overcoming Partial-Observability Caused by Sensor Limitations and Short Detection Ranges in Traffic Signal Control
Bibliographic record
Abstract
Adaptive Traffic Signal Control (ATSC) systems depend on sensor readings in order to dynamically respond to the real-time traffic stream and update signal timings. In practice, sensors do not represent the state of the intersection with complete fidelity, and may not gather information needed for optimal or even effective control. The capability of an intersection-level controller also depends on its effective detection range, i.e. the range over which the intersection receives information about the state of the traffic network. Longer effective detection range may improve the controller, assisting with tasks such as progression formation. In this paper, we begin by making an analysis of the partial-observability (PO) of a fully decentralized regional ATSC system and clarifying the PO caused by sensor limitations and short detection ranges. We then construct a decentralized controller eMARLIN+ and experimentally verify on both representative synthetic and real-world scenarios that our eMARLIN+ effectively compensates for the two sources of PO, promotes coordination, and outperforms strong baselines in minimizing intersection delay.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".