Spatial Transferability Testing of Dummy Variable Winter Weather Model Using Traffic Data Collected from Five Geographically Dispersed Weigh-in-Motion Sites in Alberta Highway Systems
Bibliographic record
Abstract
It has been an engineering practice that highway agencies collect traffic data using highway traffic monitoring techniques such as permanent traffic counts (PTCs) and weigh in motion (WIM). This research used the WIM traffic data collected for 6 years from one of six WIM sites installed and operated in the Alberta provincial highway network to develop a winter weather dummy variable model. Five other sites were used for a spatial transferability test of the estimated model. Few past studies have tested empirically whether a winter weather model developed for one site can be transferred spatially to other locations. A goal of this paper is to develop a winter weather dummy variable model using winter season traffic and weather data and then test its spatial transferability by applying the model to other geographically dispersed locations. A total of 16,746,310 vehicular records collected for 6 years spanning from 2005 to 2010 at a WIM site on a commuter road near the City of Leduc, Alberta, Canada, were used to calibrate a model. Three vehicle classes such as total traffic, passenger cars, and truck traffic were classified from raw WIM data and used for model development. Using four types of model structures differentiated from the initially developed model for each vehicle class, this research performed spatial transferability. This research shows that the developed dummy variable model can be successfully spatially transferred to the other five WIM sites. More accurate traffic estimates can be made during winter seasons by using other model structures for each vehicle type.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".