Analysing the Impact of Pedestrian Network Centrality on Segment-Level Pedestrian Density
Bibliographic record
Abstract
Studies have shown that street network centrality measures are capable of explaining a significant proportion of pedestrian activity. These studies typically employ street centreline networks that differ significantly from the networks that pedestrians use to traverse the built environment. Presently, centrality approaches are rarely applied to dedicated pedestrian network (DPNs). This creates uncertainty regarding their ability to explain pedestrian activity when derived from DPNs. This study addresses that gap by investigating the extent to which centrality metrics derived from DPNs can explain observed pedestrian densities, both alone and when controlling for other built environment variables in metro station environments in Asia. In total, four DPNs were created centred on metro stations in Bangkok, Manila, Osaka, and Taipei chosen to represent different urban typologies. Multivariate results show that centrality metrics alone explain a mere 6–24% of observed pedestrian densities when calculated on DPNs. When all factors are considered, the contribution of centrality remained consistent in most study sites but is somewhat reduced with land-use variables and proximity to rail transit revealed as the strongest predictors of pedestrian density. Pedestrian design factors were also frequently associated with pedestrian density. Finally, stronger associations between centrality and pedestrian densities were observed in the denser, more complex pedestrian environments. These findings provide insight into the performance of centrality measures applied to DPNs expanding pedestrian network research in this area.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".