The Joint Impact of Traffic Signal Control and Automated Vehicles on Traffic Efficiency, Safety and Emissions: A Deep Reinforcement Learning Approach
Bibliographic record
Abstract
ABSTRACT Recent developments in intelligent transportation systems underscore the promise of combining deep reinforcement learning (DRL)‐based traffic signal control (TSC) with automated vehicles (AVs) to improve intersection management. This study analyses how integrating DRL‐based TSC systems with AVs affects traffic efficiency, safety and emissions under varying demand levels. By simulating realistic driving behaviours and using sophisticated statistical methods, the research finds that DRL‐based TSC significantly outperforms traditional fixed‐time and actuated systems, effectively reducing congestion, emissions and conflicts. Queue length analyses reveal that DRL‐based TSC provides substantial efficiency gains, further enhanced by AVs, which reduce congestion through improved driving automation. Notably, the short‐term benefits of DRL‐based TSC at low AV market penetration rates resemble the long‐term effects of conventional systems at high AV adoption. While fuel consumption improvements under low demand are modest compared to other adaptive systems, high‐demand scenarios show significant advantages of DRL‐based TSC, with AV integration further optimising flow and reducing stop‐and‐go patterns. Safety analysis indicates that DRL‐based TSC improves intersection safety, particularly at low AV penetration, with AVs dramatically reducing conflicts. Overall, combining DRL‐based TSC with AV technology holds considerable potential for advancing traffic management, safety and environmental outcomes in urban settings.
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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) | 0.000 | 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".