Secure Fog-Edge and 5G-Enabled Architecture for AI-Driven Mobility, Real-Time Traffic Analytics, and Accessibility in Aging-Focused Intelligent Transportation Systems
Bibliographic record
Abstract
With the global population aged 65 and older expected to double by 2050, ensuring equitable access to safe, efficient, and responsive transportation systems has become a pressing societal imperative. This paper introduces a secure fog-edge and 5G-enabled architecture designed to enhance the safety and accessibility of aging populations within next-generation Intelligent Transportation Systems (ITS). The proposed framework integrates AI-powered elderly vehicle monitoring for proactive risk detection, real-time traffic analytics through decentralized edge-fog computing, and adaptive intelligent traffic lights that respond dynamically to the mobility needs of older drivers and pedestrians.Leveraging decentralized edge intelligence, blockchain-enhanced privacy safeguards, wearable health-monitoring integration, and AI-driven analytics, the system provides ultra-low-latency, resilient, and adaptive mobility services. The architecture emphasizes latency mitigation, human-centered interface design, dynamic risk evaluation, and policy-aligned cybersecurity strategies. Analytical comparisons demonstrate that local data processing combined with predictive threat modeling can substantially reduce transportation-related health risks, improve decision-making efficiency, and foster inclusive urban mobility. A novel architectural model is presented to inform the design of secure, accessible, and aging-centric ITS infrastructures.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".