TRA-901: OPTIMIZING THE LOCATION OF ROAD WEATHER INFORMATION SYSTEMS (RWIS) STATIONS – A SAMPLING DESIGN OPTIMIZATION APPROACH
Bibliographic record
Abstract
This study presents an innovative approach to the design of a road weather information monitoring system (RWIS) that optimally combines spatial data on weather-related road surface conditions with data on traffic volume over a state-wide road network. The optimization method minimizes the spatially averaged ordinary kriging variance of hazardous road surface condition (HRSC) frequencies. Since it is desired that an RWIS should also be located at high traffic demand areas, road class data is implemented in the optimization process. Spatial simulated annealing (SSA) is used to search for the optimal RWIS network design by iteratively examining each possible location and accepting designs that ameliorate a weighted sum of average kriging variance and road class detection capability. This novel approach is applied in the optimization of Minnesota RWIS network to illustrate the distinct features of the proposed method, assess the effectiveness of the current location setting, and recommend new additional stations locations. The findings of the study suggest that the method introduced in this study is useful for determining the optimal RWIS station locations and placing a few in addition to the existing stations by incorporating key elements being considered in practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".