Urban Rapid Rail Transit System Intermodality: \nIdentifying Themes In Urban Public Transit within Canada and the United States
Bibliographic record
Abstract
High-quality public transportation improves the livability of neighborhoods, particularly in car-free or car-light urban environments. Public transportation is organized in a hierarchy, with different modes of transit serving different niches of travellers. Within this hierarchy, rapid rail provides the core service, operating vehicles with the highest capacity at the highest frequency. While ridership, fare provision, and other frequency-centric metrics have been at the forefront of public transit analysis, there is growing room for spatial metrics to describe connectivity within public transit. The Intermodality Index measures the average number of public transit connections at each rapid rail station within a given network. The full Intermodality Index is a composite of five intermodal paired indices that connect rapid rail to bikeshare, bus, ferry, rapid rail, and other rail services. By looking at intermodality through the lens of individual routes/services, this index approximates spatial intermodality since these routes/services serve unique geographies. Applying this index to the rapid rail networks of Canadian and American cities, we see that it favours networks that rely on intra-agency and supplemental rail and bus connections. Cities with minimal supplemental service providers and/or shallow intra-agency bus and rail service scored lower on this index. For bikeshare, overlapping service areas were highly conducive to higher intermodality. Ferry service was also notably dependent on physical geography to encourage intermodality.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".