Pedestrian Access to Transit: Identifying Redundancies and Gaps Using a Variable Service Area Analysis
Bibliographic record
Abstract
Identifying the percentage of the population being served by a transit system in a metropolitan region is a key performance measure. This performance measure depends mainly on the definition of service area. Observing existing service areas can help in identifying existing gaps and redundancies in transit system. In the public transit industry, 400 meter (0.25 miles) buffers around bus stops and 800 meters (0.5 miles) around rail stations are commonly used to identify the area from which most transit users will access the system by foot. This research paper uses detailed origin-destination survey information to generate variable service areas that defines walking catchment areas around transit services in the Montreal region. The 400 and 800 meters service areas are greatly underestimating current coverage around transit stations. The 85th percentile walking distance to bus transit service is around 550 meters from the origin and 660 meters to the destination. A statistical model is generated to estimate walking distances to transit stations. Walking distances vary based on household and personal characteristics, trip characteristics (especially the headway), type of transit (metro, commuter rail, and bus services), and route characteristics (stop spacing). Accordingly, service areas around transit stations should vary based on the type of service being offered. The generated service areas derived from the statistical model are then used to identify gaps and redundancies in the existing transit network in Montreal region. Finally, detailed analysis examining overlapping service areas along two specific routes shows the usefulness of variable service areas in identifying areas where potential stop spacing revisions can be possible without losing coverage.
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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 | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.015 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".