Bibliographic record
Abstract
Intelligent Transportation Systems (ITS) play a pivotal role in shaping the foundation of smart cities, providing data-driven solutions for traffic management, prediction, and safety. However, these applications often face a significant challenge - data scarcity. Insufficient data limits the effectiveness of machine learning models in the context of ITS. To address this issue, this paper presents a novel data augmentation solution using Generative Adversarial Networks (GANs). By collecting sensor-based traffic speed data with contextual labels and training a GAN-based model to generate realistic traffic data for specific days and times, this research successfully proposes a solution to the problem of data scarcity. The generated data undergoes comprehensive qualitative and quantitative evaluations, demonstrating its potential to enhance ITS applications. Furthermore, the generated data is utilized to augment the training data for multiple traffic prediction models, effectively enhancing their performance. This approach opens new avenues for the development of intelligent and sustainable transportation systems, ultimately contributing to the advancement of smarter and more resilient cities.
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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".