Statistical Investigation of Truck Type Distribution on Cold Region Highways During Winter Months
Why this work is in the frame
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Bibliographic record
Abstract
Recent research on impact of weather interaction on classified traffic volume variations on provincial highways in cold regions showed that the total truck volume is not affected with severity of snowfall and cold temperature. However, no research is conducted to analyze the variations in truck distributions, despite of its importance for truck counting and monitoring program. Described in this paper is the statistical investigation of association of truck type distribution on cold region highways during severe winter months and seasons in a year. The investigation is based on weigh-in-motion data collected from six sites located on five provincial highways in Alberta, Canada. Trucks were classified into three types such as single unit, single trailer, and multi trailer using the FHWA vehicle classification scheme. Two statistical tests namely Chi-squared test and Binomial probability test were applied to analyze the distributional change in three different truck classes during high snowfall and low temperature conditions. The analysis suggested that the truck type distribution does not change from winter to non-winter season for regional commuter road (Highway 2A), long distance roads (Highway 2 and Highway 16). Also, no change in truck distribution from month to month was noticed during sever winter months. Consistent results were not found for special roads such as Highway 44 due to difference in road user characteristics. The study findings have practical implications for rationalization of the length and frequency of traffic counts including classified traffic monitoring programs throughout the year. The knowledge about independency of truck type distribution with various seasons is likely to help in effective traffic monitoring and estimation of the highway planning and design parameters like Truck Annual Average Daily Traffic (TAADT), Truck Average Daily Traffic (TADT) and Design Hour Truck Volume etc.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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