Bibliographic record
Abstract
The study of accessibility - the ease of reaching destinations - by public transport has made huge advances thanks to the availability of standardized, routable transit schedule data.The General Transit Feed Specification (GTFS) has provided researchers with a vast trove of machine-readable data allowing for highly detailed spatio-temporal modelling of scheduled transit operations.Yet it is well established that in the real-world schedules are imperfect - vehicles often run late, get bunched, miss transfers, arrive too full for anyone to board, and otherwise behave in predictably unpredictable ways. Schedule data alone cannot possibly account for this distinctly stochastic component of much transit service, which to date has been considered separate from accessibility analysis under the umbrella of ``reliability''. This dissertation takes the perspective that transit service is reliably unreliable and will continue to be so until humans are taken out of the equation. Detailed observations of actual service can be used to construct more realistic models for estimating travel times and thus accessibility via transit.Chapter 2 introduces a novel method of converting a detailed GPS record of transit fleet locations into a retrospective GTFS package. This backward-looking "schedule'' format allows the same tools developed for schedule-based GTFS analysis to be applied in Chapter 3 to a more accurate depiction of actual transit accessibility. The findings indicate that models of transit accessibility based on schedule data alone tend to produce substantial overestimates of accessibility and systematic spatial errors by failing to account for normal irregularities in service provision. Chapter 4 points toward a way of better suiting available GTFS analysis tools to actual transit service by addressing the problem of imperfect information in modelled route choice. The travel time implications for a large minority of trips are shown to be substantial. Transit accessibility research has come a long way in the last decade and has a long way yet to go. Models based on schedule data alone should give way in many cases to models based on service as actually provided, acknowledging that schedules may guide but rarely constrain the transit services that passengers actually use every day.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.009 |
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".