Using Realtime GTFS to generate easy-to-use transit accessibility measures under travel time uncertainty
Why this work is in the frame
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Bibliographic record
Abstract
Previous studies on measuring transit accessibility under travel time uncertainty often introduced complex measures based on non-standard data formats, hindering reproducibility and replicability in research and planning. To address this, we present a practical framework that leverages a standardized format for real-time transit data: Realtime General Transit Feed Specification (GTFS), to generate easy-to-use transit accessibility measures under travel time uncertainty. This framework first produces two datasets by correcting Scheduled GTFS data using Realtime GTFS information: Realtime P50 GTFS and Realtime P85 GTFS, which are used to compute two accessibility measures : median-corrected accessibility (using Realtime P50 GTFS) and dispersion-corrected accessibility (using Realtime P85 GTFS). These accessibility measures are applied in Columbus, Ohio, USA for an empirical study examining how overlooking travel time uncertainty issues can distort the analysis results of healthcare accessibility, inequality, and new transit project evaluation. Results indicate that scheduled accessibility (using Scheduled GTFS data) which overlooks travel time uncertainty overestimates healthcare accessibility by approximately 10.97 %. Moreover, this oversight fails to capture the benefits of the new transit service in improving accessibility and reducing inequality. Furthermore, although findings consistently suggest that lower-income neighbourhoods experience greater gains in healthcare accessibility compared to wealthier counterparts, our analysis unveils statistically significant differences when using the scheduled and dispersion-corrected accessibility measures. These findings underscore the importance of incorporating travel time uncertainty into public transit planning and evaluation. Our framework allows transit authorities and researchers to accurately measure accessibility and evaluate projects under travel time uncertainty using a standardized data format.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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