Effectively and Efficiently Supporting Predictive Big Data Analytics over Open Big Data in the Transportation Sector: A Bayesian Network Framework
Bibliographic record
Abstract
Today, various types of valuable data can be collected with ease and at a rapid pace. In recent years, many governments, researchers, and organizations have been driven by open data pioneers, to make their data available for public. Transportation data, such as public bus performance data, is an example of open big data. The analyzing of these open big data can be used in social services. For example, bus service operators might get a vision into time delays in bus services by processing and mining public bus performance data. Then, making ameliorative steps (e.g., adding more buses, rerouting some bus routes, etc.) results in improving the feeling of the passenger. We provide a Bayesian framework, which is applied on big data obtained from transportation system. Specifically, a number of Bayesian networks have been used in our framework to predict whether a bus will arrive late or early at a specific bus stop. We investigate and establish the optimum network settings and/or parameter permutations for each (bus stop, bus route, arrival time)-triplet. The results demonstrate that the proposed Bayesian framework effectively supports predictive analytics on big transportation data collected from the City of Winnipeg, Manitoba, Canada.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".