Counting in: A methodological framework for the accessibility assessment of on-demand transit
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it