Explainable reinforcement learning for improved traffic signal control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Reinforcement learning has become a popular approach for traffic signal control, but its complexity often hinders practical application. To improve this, we propose a deep Q-network framework with an attention mechanism inspired by how traffic police manage intersections. This model aggregates vehicle data directly, avoiding the need for lane-based structures, and shows state-of-the-art performance across various scenarios. On a public dataset, it outperformed previous methods, achieving a 44% reduction in travel time and over 50% fewer queue lengths, compared to fixed-time control. Additionally, on a self-collected dataset, it improved the average travel time by 17.3% and queue length by 12% during peak hours. The method balances travel time, delay, and throughput while adapting to various traffic patterns, enhancing its effectiveness for traffic signal optimization. By visualizing attention weights on key vehicles, we provide transparency in decision-making, fostering trust among traffic agencies and the public.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it