Conceptual Modeling and Smart Computing for Big Transportation Data
Bibliographic record
Abstract
Technical advancements in recent decades have led to generation and collection of much more data at a rapid rate from a wide variety of rich data sources. The popularity of initiates of open data has also encouraged the sharing of these big data so that they have become publicly accessible. Examples of these big data include transportation data. Analyzing and mining these big transportation data help users (e.g., commuters, city planners) to take appropriate actions (e.g., making wise decisions), which in turn help building a smarter city. This leads to smart computing. Moreover, contents of available big transportation data may vary among cities, which lead to the conceptual modeling to describe- at a high level of abstraction-the semantics of data analytic and mining software applications on big transportation data. In this paper, we present conceptual modeling and smart computing for big transportation data. We illustrate our idea with real-life big transportation data from the Canadian city of Winnipeg and to show its practicality in real-life data.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".