Development and Balance Evaluation for Land Use and Transport Interaction Using Node‐Place Model and Data Envelopment Analysis
Bibliographic record
Abstract
To meet the evolving demands of land use and transport interaction (LUTI) assessment within China’s national territory spatial planning (NTSP) system, this paper introduces the level of development (LoD) and the level of matching (LoM) evaluation models, based on the traffic facility and land use factors. The LoD model, founded on the modified node‐place model, provides a comprehensive assessment of the traffic facility and land use development scales. Conversely, the LoM model, grounded in data envelopment analysis methods, evaluates the relative relationship between transport services and the travel demand generated by land use. The integrated use of LoD and LoM can both reflect the development scale and matching status between traffic services and travel activities, which are crucial contents in the planning works, especially within the NTSP framework. The proposed models are tested in the city of Guangzhou, and the LoD values exhibit peaks in central urban zones, suburban towns, and areas adjacent to railway transit, with a decline observed in rural farmland and ecological regions. In contrast, the LoM distribution performs a distinct pattern, highlighting numerous underperforming areas with congestion or idle problems in urban centers, alongside well‐coordinated regions showcasing a balance between traffic facilities and land uses in rural regions. Furthermore, the LoM scores revealed frequent instances of facility crowding in urban regions and intensive occurrences of facility idleness in rural areas. By marking regions with low LoD scores, the LoD model finds suitable application in determining the urban development border, essential for restricting land development and preserving farmland and ecological areas. Meanwhile, LoM aids in improving urban renewal efforts by assessing and optimizing the balance between intensive land uses and limited traffic facilities. Validated against the existing metrics, the combined use of LoD and LoM efficiently captures the most details of the LUTI process at the lowest computational cost.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".