Counting in: A methodological framework for the accessibility assessment of on-demand transit
Bibliographic record
Abstract
This paper addresses an existing methodological and empirical gap by presenting a framework for conducting a regional cumulative accessibility analysis of a transit network with an on-demand component and demonstrating its application to the context of a mid-sized Canadian city. We rely on the concept of accessibility as a performance metric and propose a methodological approach for inferring inputs for accessibility calculations from actual on-demand operations in Edmonton, Canada, to develop the tool for the accessibility assessment of a transit network with an on-demand component. Our empirical findings show that on-demand zones that were introduced with a redesigned bus network saw the largest gains in transit accessibility, and we identified that excluding on-demand transit from accessibility analysis underestimates the systemic effect of the bus network redesign. While the use of the accessibility framework for the assessment of a transit system with an on-demand component offers a meaningful and comprehensive measure of the success both for the planning purposes at the stage of design and for the post-implementation evaluation of either incremental or systemic changes, on-demand service standards must be developed to ensure consistent service provision of on-demand services throughout the day. On the other hand, informed by the findings of our study and publicly available information about the operational costs of service provision we identified that on-demand service provides operational savings over fixed routes if ridership in the area is less or equal to eight passengers an hour.
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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 | 0.042 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".