Transportation Analytics with Fuzzy Logic and Regression
Bibliographic record
Abstract
Bus riders desire precision and accuracy when using the transit system. While the transit system is responsible for maintaining and delivering public transportation services for the city residents, they rely on idealized assumptions regarding real-world bus driving conditions. The published bus schedule seems to assume that the buses move at a uniform speed at all times, which leads to bus arrival times that are imprecise and inaccurate. Busses can arrive early, late, or on time. Given that bus stops cannot have a dynamic schedule, it is logical to create a schedule accounting for the changes in traffic patterns. Hence, in this paper, we present a transportation analytics solution. It captures imprecision via fuzzy logic. It takes in account lane closures (for construction sites) and traffic count when predicting bus on-time performance via regression. Evaluation on real-life data covering close to 6,000 bus stops in the Canadian city of Winnipeg demonstrates the practicality of our fuzzy logic- and regression-based transportation analytics solution in predicting whether buses arrive the bus stops early, on time, or late in various time periods of the day. This helps in building a smart city.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".