Benefit–cost Assessment of Automatic Vehicle Location (AVL
Bibliographic record
Abstract
AVL has been used extensively in public transit, law enforcement, and EMS applications (among others), and is garnering more and more interest with the highway maintenance community. Sponsored by the Kansas DOT, The University of Kansas conducted a study of the use of AVL for highway maintenance activities, especially snow removal. State DOTs and other transportation agencies were surveyed with respect to their use or potential use of AVL. At the time of the survey, only 8 states had deployed AVL, in addition to several municipalities and one Canadian province. None of the surveyed agencies had conducted quantitative assessments of the benefits of system deployment. Qualitative and perceived benefits taken from the aggregated survey data were used to develop estimates of benefits likely to be realized from an AVL deployment. Savings from improved fleet management, paperwork reductions, and reductions in snow-related crashes were compared with the system investment and maintenance costs. Costs for system wide deployment were estimated to be about $9,000,000 with about $800,000 needed for maintenance annually. Benefit/cost ratios were calculated for three deployment schedules based on conservative assumptions and then based on moderate assumptions. The analysis estimated B/C ratios would be at least 2.6 and probably closer to 25. This paper elaborates on the results of the survey and details the methodology used in the analysis. Key words: AVL benefitsITS—maintenance—snow removal
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".