AI-Driven Smart Traffic Management: Enhancing Urban Mobility with Predictive and Adaptive Intelligence
Bibliographic record
Abstract
Urban traffic congestion continues to pose challenges to mobility, productivity, and sustainability in smart cities. Traditional traffic control systems, based on fixed-time schedules, cannot dynamically adapt to fluctuating traffic conditions. This paper presents a dual-layer intelligent traffic signal control framework that integrates deep learning-based traffic flow prediction with reinforcement learning-driven adaptive signal optimization. Short-term traffic flow is predicted using CNN and RNN models trained on the PeMS dataset, while real-time detection of vehicles and emergency prioritization is enhanced through GPS and audio-based sensing. The reinforcement learning layer, implemented with DQN and PPO agents, optimizes traffic light phases using reward signals that balance wait-time reduction, flow efficiency, and emergency vehicle passage. Experimental results demonstrate that the proposed system reduces average waiting time from 120 s to 84 s, improves flow efficiency from 65 % to 88 %, and increases emergency prioritization accuracy to 92 %. The framework, deployed on real-world data streams, highlights the potential of combining predictive deep learning with adaptive reinforcement learning for scalable and sustainable urban traffic management.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".