Smart City Transportation Data Analytics with Conceptual Models and Knowledge Graphs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Technological advancements have led to easy and rapid generation and collection of huge amounts of varieties of data from of wide ranges of rich data sources. These big data may be of different levels of veracity, including precise data and imprecise or uncertain data. Embedded in the data are valuable information and useful knowledge that can be discovered by data analytics. Discovered information and knowledge may help to build a smart city and then a smart world. In this paper, we focus on making good fusion of conceptual modelling and knowledge graphs to capture essential data about public transportation (e.g., buses). Specifically, our conceptual model and knowledge graph capture information regarding bus arrival and departure. Some of the captured data can be uncertain (e.g., timestamp, GPS locations of buses) due to the limitations of the measuring devices and methods to collect the data. Nonetheless, the models are helpful in smart city big data analytics of these public transportation data. The discovered knowledge produces insights to users (e.g., city planners, policy makers), which in turn help them to build a smart city.
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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 | 0.002 |
| 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 it