Key corridor identification in multi-objective highway networks based on feature lines
Bibliographic record
Abstract
To enhance the overall accessibility and operational efficiency of highway networks, this paper proposes an integrated analytical approach based on node importance, network reliability, and critical link identification to identify key transportation corridors within highway networks. Initially, a comprehensive node importance measurement method is developed by integrating static geometric characteristics and dynamic traffic attributes of complex networks. The weights of static indicators are calculated using an improved entropy weight method, while the dynamic importance of nodes is assessed based on the h-index, resulting in a ranked node importance list. Subsequently, from the perspective of network reliability, critical nodes are identified and ranked by simulating node failure scenarios through attack strategies, evaluating their impact on network connectivity and travel time. Further, critical links are identified utilizing the Stochastic User Equilibrium (SUE) model and Ant Colony Optimization (ACO). Finally, a multi-objective key corridor identification method based on feature lines is formulated by comprehensively considering node importance, network reliability, and critical road segments. An empirical analysis is conducted on the highway network across 11 counties/districts of Zhaotong City, Yunnan Province. Three key transportation corridors are ultimately identified:Ludian County-Zhaoyang District-Daguan County-Yanjin County, Ludian County-Zhaoyang District-Daguan County-Yongshan County, Ludian County-Zhaoyang District-Yiliang County.
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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 | 0.001 | 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".