3D walking accessibility in practice: exploring the imperfections from data, method, and assumptions of human-space interaction
Bibliographic record
Abstract
Current accessibility measures predominantly focus on motorized transport and 2D space. Limited studies have explored the operationalization and errors of 3D accessibility. This study contributes to the ontology of imperfection in modeling morphology-sensitive walking accessibility. We computed the accessibility to healthy food, considering different combinations of node-snapping processes (2D vs. 3D), transport infrastructures, cost functions (slope-unaware vs. least-time vs. least-effort), and trip orders (outbound first vs. inbound first). Findings showed that conventional 2D routing underestimates travel time by at least ∼1.5 minutes and ∼2 minutes for 50% of the population compared to the least-time and least-effort 3D routings. Besides, the process of connecting the location (2D vs. 3D) to the network can generate unrealistic trips to unrealistic places that can over or underestimate travel costs. Last, with least-effort routing, the difference between inbound and outbound round trips is greater than one minute for at least 8.37% of the population, reflecting the deficiency of using outbound trips or their round trip to accurately assess the access situation. Our findings identified spatially varied errors modulated by different combinations and interplay of data, methods, and the assumption of human-space interaction, highlighting the importance of a context-and-place-specific methodology to avoid the one-size-fits-all analytical framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| 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 | 0.007 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".