Missing the Connection? A case study approach to understanding effective public transit transfers in dispersed lower density cities
Bibliographic record
Abstract
The 'network effect' is a public transit operating approach aimed at serving the complex travel patterns of dispersed lower density cities. The approach relies on effective transfers between transit modes. In compensation for the inconvenience of transfer, passengers are rewarded with a service providing access to a wide, rather than limited, geographic area. Whilst the theory is now well established, analysis of its success in practice is less well researched. This paper adds to knowledge on the network effect by comparing feeder transit service levels and rates of transfer observed in two case study locations. Feeder transit quality of service is measured by (i), the number of services provided on each route, and (ii), the timetabled wait time between feeder and trunk services (and vice versa). The paper compares rates of transfer between bus and train at stations on the Dandenong Line in Melbourne with the eastern branch of the Montreal metro Green line to Honore Beaugrand. The paper reports on on-going analysis that seeks to identify relationships between these variables and rates of transfer observed at stations along both lines. In particular, the analysis is interested in factors influencing the higher rates of transfer seen in the Montreal case study. The findings will be important for any transit agency looking to benefit from the theoretical advantages of the network effect in suburban land forms common to Australian and North American cities. They are of particular interest in Melbourne given recent investment commitments to grade separations and the Melbourne Metro rail tunnel.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".