Municipal Infrastructure Asset Levels of Service Assessment for Investment Decisions Using Analytic Hierarchy Process
Bibliographic record
Abstract
A given infrastructure system has different levels of service (LOS) for different users. To date, limited work has been done to combine these LOS to an asset level LOS. In addition, existing methods to determine LOS are based on the quantitative performance measures related to the capacity of the infrastructure systems. These methods neglect other qualitative factors, for example, neighborhood safety and appearance. This paper describes a proposed asset level of service (ALOS) determination methodology, which can be integrated with decision support systems (DSS) as a performance indicator. The proposed ALOS is a composite LOS for different users of the infrastructure system. Incorporation of ALOS with DSS will aid the municipalities in producing improved resource allocation plans in compliance with service standards, applicable codes, and regulations. In this paper, a framework of the development of ALOS in municipal infrastructure systems is presented. The analytical hierarchy process is used to model the ALOS. The developed framework is then applied to calculate the ALOS for the municipality/urban roads, to combine LOS for vehicle users, bicyclists, and pedestrians; accounting for qualitative factors, such as neighborhood safety and aesthetics.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".