Using Web Mining to Support Low Cost Historical Vehicle Traffic Analytics.
Why this work is in the frame
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Bibliographic record
Abstract
Analyzing historical vehicle traffic data has many applications including urban planning and intelligent in-vehicle route prediction. A common practice to acquire this data is through roadside sensors. This approach is expensive because of infrastructure and planning costs and cannot be easily applied to new routes. In this paper, a low-cost Web mining approach is proposed to address these limitations. Our system gathers information about vehicle commute times, accidents, and weather reports from heterogeneous Web sources. Information from these sources can be combined to support road traffic analytics. We illustrate the utility of our system through a clustering analysis that investigates the traffic patterns of the busiest highway in Calgary along with factors having the most impact on commute time. The analysis shows that most of the accidents are localized around a small section of the highway near the city center and that the commute time in this segment is significantly more than that in other segments. Bad weather increases the typical evening rush hour commute time by 60% for days with moderate accidents and by a factor of 100% for days with large number of accidents. Overall, commute times can vary by a factor of 4 depending on accidents and weather. Keywords-road traffic; clustering; data analysis; Web mining; traffic management
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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 | 0.001 | 0.001 |
| 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 it