Deep Reinforcement Learning for IoT-Based Smart Traffic Management Systems
Bibliographic record
Abstract
The increasing complexity of urban traffic networks demands more intelligent and adaptive solutions for traffic management. This paper presents a novel approach utilizing Deep Reinforcement Learning (DRL) in conjunction with Internet of Things (IoT) technology to develop a smart traffic management system aimed at optimizing traffic flow in real-time. IoT devices, including sensors, cameras, and connected vehicles, provide comprehensive, high-resolution traffic data, which is processed by a DRL-based algorithm to dynamically control traffic signals and manage congestion. The proposed system continuously learns from evolving traffic patterns, adapting its decision-making to optimize key metrics such as vehicle throughput, travel time, and energy consumption. Compared to traditional traffic management systems, the DRL-based approach exhibits superior performance in handling dynamic traffic environments, reducing bottlenecks, and minimizing delays. Extensive simulations demonstrate that the integration of DRL with IoT enhances the system’s ability to manage complex, real-world traffic scenarios more efficiently. This research contributes to the growing body of work in smart city development, offering an innovative framework for the deployment of intelligent traffic management systems. The study highlights the potential of DRL and IoT technologies to transform traffic management, promoting sustainable urban mobility and improving overall traffic efficiency..
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".