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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".