The application of knowledge management to support the sustainable analysis of urban transportation infrastructure
Bibliographic record
Abstract
This paper presents a semantic framework for supporting the cost–benefit analysis in urban transit rehabilitation decisions. The use of semantic representations of decision parameters allows for more effective knowledge management practice and easier accumulation and access of corporate knowledge regarding balancing traditional construction investments with the costs to the environment, local business, and impacts on traffic. A sample illustrative case was considered by this study. It includes a comparison of a hypothetical scenario of building a monorail to replace an existing streetcar in one of Toronto's most congested streets: King Street. A microscopic simulation model for the King Street route has been developed and used in comparing the status quo to the proposed scenario in terms of impacts on traffic performance. A cost–benefit analysis has been conducted to assess the feasibility of both options. The study investigated direct costs such as monorail construction cost, streetcar system removal cost, and operating and maintenance costs of both systems. The sustainability-related costs included user costs and accident costs.Key words: sustainable infrastructure, knowledge management, cost–benefit analysis.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".