Integrating Knowledge and Data Driven Methods to Generate Synthetic Mobility Data
Bibliographic record
Abstract
Urban planning relies on the integration of mobility data from various sources. Analyzing this data can enhance decision-making for transportation services and improve multimodal transportation planning, thereby improving residents’ quality of life. However, traditional data collection methods are often error-prone, costly, and raise privacy concerns. Synthetic data offers a scalable and privacy-preserving alternative for filling gaps in real-world datasets. This paper proposes a hybrid approach that combines knowledge-driven and data-driven methods to generate realistic mobility trajectory data. Mobility data from multiple sources, including demographic statistics and geospatial information, is aggregated and used to simulate traffic patterns with the SUMO traffic simulator. A GAN-based model, specifically CTGAN, is then employed to refine and expand the dataset by generating additional synthetic mobility trajectories. The integration of multiple data sources in this study enhances the availability, diversity, and representativeness of mobility data, supporting more effective urban planning and transportation analysis.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".