Statistical Investigation of Truck Type Distribution on Cold Region Highways During Winter Months
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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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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 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".