Calculating place-based transit accessibility: Methods, tools and algorithmic dependence
Bibliographic record
Abstract
To capture the complex relationships between transportation and land use, researchers and practitioners are increasingly using place-based measures of transportation accessibility to support a broad range of planning goals. This research reviews the state-of-the-art in applied transportation accessibility measurement and performs a comparative evaluation of software tools for calculating accessibility by walking and public transit including ArcGIS Pro, Emme, R5R, and OpenTripPlanner using R and Python, among others. Using a case study of Toronto, we specify both origin-based and regional-scale analysis scenarios and find significant differences in computation time and calculated accessibilities. While the calculated travel time matrices are highly correlated across tools, each tool produces different results for the same origin-destination pair. Comparisons of the estimated accessibilities also reveal evidence of spatial clustering in the ways paths are calculated by some tools relative to others at different locations around the city. With the growing emphasis on accessibility-based planning, analysts should approach the calculation of accessibility with care and recognize the potential for algorithmic dependence in their calculated accessibility results.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".