Comparative Study on Characteristics of Urban Road Network in Station Catchment Area between China and Other Countries for Station-City Integration
Post-publication record
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Bibliographic record
Abstract
The urban road network is one of the most important factors affecting urban traffic operation in station catchment areas, as well as the main factor in station-city integration. China’s high-speed railway has developed rapidly, and station catchment areas encompassed by station-city integration have emerged as city planning and urban design aims. However, the differences in urban road network characteristics in the station catchment area between China and other countries have not been adequately researched yet. Considering 20 station catchment areas encompassed by the station-city integration as examples, this study analyzes the intersection quantity and network density in station catchment areas to compare the characteristics of urban road networks in China with those in Europe, North America, and Japan. Combined with the square block model calculation, we found the following. (1) The network density in non-China cases is concentrated in 16–22 km/km2. The Honkong West Kowloon Station and Shapingba Station approach this range, while the Shanghai Hongqiao Station and Hangzhou East Station feature considerably lower values than this range. (2) The intersection quantity in non-China cases is concentrated in 225 pcs/km2. Except for that of the Honkong West Kowloon Station, the values for the Shapingba Station, Shanghai Hongqiao Station, and Hangzhou West Station are lower than this range. (3) Developing small-scale blocks by gridding has an optimal effect on station catchment areas within the side-length range of 47.1–97.5 m. (4) The current situation of the entire urban road network and the specifications for the design codes of the road network exhibit a certain correlation with the road network characteristics of the station catchment areas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".