A Resilient Intelligent Traffic Signal Control Scheme for Accident Scenario at Intersections via Deep Reinforcement Learning
Bibliographic record
Abstract
Deep reinforcement learning methods have shown promising results in the development of adaptive traffic signal controllers. Accidents, weather conditions, or special events all have the potential to abruptly alter the traffic flow in real life. The traffic light must take immediate and appropriate action based on a reasonable understanding of the environment. In this way, traffic congestion would be prevented. In this paper, we develop a reliable controller for such a highly dynamic environment and investigate the resilience of these controllers to a variety of environmental disruptions, such as accidents. In this method, the agent is provided with a complete understanding of the environment by discretizing the intersection and modifying the state space. The proposed algorithm is independent of the location and time of accidents. If the location of the accident changes, the agent does not need to be retrained. The agent is trained using deep Q-learning and experience replay. The model is evaluated in the traffic microsimulator SUMO. The simulation results demonstrate that the proposed method is effective at shortening queues when there is disruption.
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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 | 0.001 |
| 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 | 0.000 | 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".