Combining Capital and Operating Expenditure Costs in Vehicular Roadside Unit Placement
Bibliographic record
Abstract
This paper considers the problem of vehicular roadside unit (RSU) placement so that the sum of capital expenditure (CAPEX) and operating expenditure (OPEX) costs is minimized. The minimum cost solution considers these two cost contributions jointly when making RSU placement decisions. The input to the placement process consists of historical vehicular traffic traces and a set of candidate site locations from which RSU placements are chosen. An integer linear program (ILP) formulation is first given that computes the minimum cost placement based on the input traffic traces and candidate locations. A practical algorithm is then introduced that solves a relaxed version of the ILP and uses a novel rounding procedure to obtain real RSU placements. The algorithm takes into account the energy costs incurred by vehicular requests when the latter are scheduled using a minimum energy online scheduler. Performance results are presented that show that the proposed algorithm performs well compared to the case where placements are done without jointly considering both CAPEX and OPEX cost components.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".