Determining the Initiation Threshold of Underground Road Network Construction in High‐Intensity Development Areas: A New Methodology Considering Resilience
Bibliographic record
Abstract
Assessing the resilience of road networks in high‐intensity urban development areas is crucial for ensuring sustainable urban growth in the face of increasing traffic demands. Underground road networks are a key solution to alleviating surface traffic congestion and optimizing urban spatial utilization. By enhancing shared mobility on the surface, these networks contribute to reducing vehicle emissions and mitigating environmental pollution. This study explores the optimal conditions for initiating underground road network construction in high‐density development areas. Using a spatiotemporal consumption model, the research calculates the maximum traffic capacity of the network while considering land use characteristics to assess traffic demand. The resilience of these road networks is evaluated through structural indicators that measure their resistance to damage and overall stability. The findings indicate that when the demand‐to‐capacity ratio of the road network ranges from 0.865 to 0.870, the existing road capacity becomes inadequate, necessitating the construction of underground networks to alleviate surface congestion. This study provides both theoretical guidance and practical insights for the planning and development of underground road networks, along with strategies to improve surface environmental quality.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".