Integrating Multimodality and Partial Observability Solutions Into Decentralized Multiagent Reinforcement Learning Adaptive Traffic Signal Control
Why this work is in the frame
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Bibliographic record
Abstract
Adaptive Traffic Signal Control (ATSC) systems leverage sensor data to dynamically adjust signal timings based on real-time traffic conditions but they often suffer from partial observability (PO) due to sensor limitations and restricted detection ranges. This study addresses PO in fully decentralized ATSC systems by introducing eMARLIN-T, a controller designed to enhance performance by incorporating historical information in the decision-making process. Additionally, ATSC systems are commonly optimized to improve the performance of the general traffic, ignoring the impact on transit. On the other hand, traditional transit signal priority (TSP) strategies, which overlay preferential strategies for transit vehicles onto general traffic fixed signal plans, often lead to negative impacts on the general traffic. Thus, this paper tackles the challenge of optimizing traffic signals to benefit both public transit and general vehicular traffic. To address this, a novel decentralized multimodal multiagent reinforcement learning (RL) signal controller, eMARLIN-T-MM, is developed. This controller integrates a transformer-based encoder for transforming the state observations into a latent space and an executor Q-network for decision-making. Tested on a simulation of five intersections in North York, Toronto, eMARLIN-T-MM significantly reduces the total person delays by 58% to 74% across various bus occupancy levels compared to pre-timed signals, outperforming the other decentralized RL-based ATSCs. In addition, eMARLIN-T-MM can automatically adapt to changes in the levels of occupancy, allowing it to optimize the intersection performance in response to varying transit and traffic demands.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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