A Regression-Based Data Science Solution for Transportation Analytics
Bibliographic record
Abstract
In the current data-driven era, large volumes of data are generated and collected at a rapid rate. Examples of these big data include transportation data (e.g., public transit data). Integration of different transportation data, as well as reuse of past knowledge and information on public transit, can be for social good (e.g., can help improve public transit services for bus riders). To elaborate, bus riders wish to have a precise and accurate schedule for their transit system. On-time bus arrival and departure are desirable as an early departure or late arrival of bus may lead to rider inconvenience. To achieve this goal, we present in this paper a regression-based data science solution for transportation analytics. It integrates heterogeneous data regarding bus stops, bus arrival times, road networks, traffic counts, construction sites, lane closures, etc. It reuses past knowledge and information discovered from historical data for handling future situations. Evaluation on real-life transportation data from a Canadian city of Winnipeg shows that our regression-based data science solution led to a high R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score. It demonstrates the practicality of our solution in transportation analytics and bus arrival time prediction, as well as the benefits of data integration and information (and knowledge) reuse. Moreover, it is important to note that, although we illustrate our solution on Winnipeg transit data, our solution is expected to be reusable for transportation analytics at other locations.
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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".