Intelligent Transportation Systems Utilizing UAVs: Integration with IoT and Machine Learning
Bibliographic record
Abstract
The rapid urbanization and growing transportation demands have intensified challenges in modern transportation systems, such as traffic congestion, accidents, and environmental concerns. Intelligent Transportation Systems (ITS) have emerged as a solution, leveraging advanced technologies to improve efficiency and safety. This paper explores the integration of Unmanned Aerial Vehicles (UAVs), Internet of Things (IoT), and Machine Learning (ML) in ITS, highlighting their potential to revolutionize transportation management. UAVs serve as agile and cost-effective data acquisition platforms, equipped with cameras, Light Detection and Ranging (LiDAR), and IoT sensors for real-time monitoring. IoT facilitates seamless connectivity, enabling real-time data transfer and processing, while ML algorithms analyze this data to provide actionable insights for predictive traffic management and accident prevention. Case studies demonstrate significant improvements in traffic flow, emergency response times, and infrastructure monitoring. Challenges such as UAV battery limitations, bandwidth constraints, and privacy concerns are discussed alongside future directions, including quantum computing integration and swarm UAV technology. This paper provides a comprehensive overview of the current state of UAV-IoT-ML integration in ITS and identifies key areas for innovation to address modern transportation challenges and achieve sustainable mobility. Tables and figures elucidate the discussed concepts, making this paper a valuable resource for researchers and practitioners in the field of smart transportation systems.
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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 | 0.002 |
| Open science | 0.000 | 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".