Road weather information system stations — where and how many to install: a cost benefit analysis approach
Bibliographic record
Abstract
This paper presents a cost–benefit based approach to the problem of finding the optimal location and density of road weather information system (RWIS) stations over a regional road network. The novelty of the proposed method lies in the models that can be used to estimate the benefits of RWIS information, including reduced maintenance costs and collisions, thus determining the optimal number and location of a RWIS network. A case study based on the existing RWIS network in Northern Minnesota in the US is used to show the application of the proposed approach. Linear regression models are developed for the annual maintenance costs and the expected number of collisions that could occur on two types of highways delineated by the existence of a RWIS station nearby. The calibrated models are then applied to individual highway segments defined on the basis of a uniform grid system to determine the expected benefit of having a RWIS station installed. These benefits along with RWIS installation and maintenance costs are converted into net benefits and then the net present value, which are then used in determining the optimal number of RWIS stations and prioritizing the candidate locations. It was found from the case study that a total of 45 stations would provide the best return of investment with a 25 year net benefit of approximately $6.5 million and a life-cycle benefit-to-cost ratio of 3.5.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".