Performance evaluation and alternative optimization model of light rail transit network projects: A real case perspective
Bibliographic record
Abstract
Light rail transit network is essential to the development of urban public transportation, and the aim of this study is to provide a scientific, efficient, and new methodology to measure and assess the pros and cons of various planning schemes for light rail transit (LRT) network, which can help guide the process of alternative prioritization and decision-making support. More specifically, through establishing the multi-attribute assessment index system, and pondering the combinational weighting model (consistent matrix analysis and information entropy methods), a data-driven and flexible multi-criteria matter-element decision-making (MCMEDM) model for LRT network optimization is constructed in this work. Furthermore, objectivity, impartiality, rationality, and validity of the proposed model are discussed and verified by a didactical case with real datasets in Jining, China. The modeling analysis (quantitative procedures) results and findings reveal that this approach is reliable and applicable to appraise and prioritize the LRT networks, avoiding the decision biases due to human or other factors. Additionally, the proposed framework can offer urban planners, managers, decision and policy makers the appropriate comments and suggestions of identifying the superior alternative (project), as well as serve as a guideline or reference for other cities.
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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 | 0.001 | 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 | 0.000 |
| 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".