Data-Driven Sustainability Validation of Winter Traffic Model through Spatial Transferability of the Model’s Parameters between Functionally Homogeneous and Heterogeneous Highway Segments
Bibliographic record
Abstract
Transportation agencies in the cold region are responsible for developing winter traffic models and verifying their sustainability to save financial and human resources while enhancing the suitability of the developed models. To do this, they operate traffic monitoring sites to collect traffic volume and loading data in their network using technologies such as permanent traffic counters (PTCs) and weigh-in-motion (WIM). None of the previous studies have conducted spatial transferability of the winter traffic models’ parameters between homogeneous and heterogeneous road segments during the winter season. This research pursues this using traffic data collected from six WIM sites in Alberta, Canada. Winter traffic models were developed for two WIM sites that serve commuter traffics. The other four WIM sites serving different travel populations besides commuter traffic were exhaustively utilized to test the developed models. The raw WIM data were aggregated into three vehicle types to develop winter traffic models by associating traffic data with climatic information. Two spatial transferability tests for the developed models were designed and carried out. The first test was conducted between the two modeling sites for which the winter traffic models were developed. The first experiment pursued a cross-spatial transferability test between homogeneous road segments. The second experiment tested the transferability of model parameters between heterogeneous road segments that represent a different road function other than commuter type. The models’ parameters developed for the two commuter segments were transferred to the other four sites to test their spatial transferability. This research has demonstrated that the winter traffic models developed for the roads serving one specific travel population can be transferred with high accuracy to homogeneous and heterogeneous road segments. It revealed that a more suitable model structure could be selected for each site and vehicle class, considering the accuracy of the test results. This research contributes to planning and designing traffic monitoring or weighing site deployment to save financial and human resources.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".