Measuring the timing between public transport provision and residential development in greenfield estates
Bibliographic record
Abstract
The timing of public transport provision in newly established suburbs on the urban fringe is a major concern for residents. It is argued that if public transport were available when residents start moving to a new suburb, they are more likely to use it. Despite this, the timing of public transport service provision relative to residential development is generally unknown. Using a case study of Melbourne, Australia, this article provides a methodology to measure the timing of bus provision relative to residential development. Information from Precinct Structure Plans, Census data, public transport timetables, and a spatial analysis based on Open Street Map, Metromap and Google Earth, were used. Results show that new communities on Melbourne’s urban fringe had to wait 3–4 years on average for a bus service to be implemented. About one quarter (24%) of the communities were already served by a bus service when residents started to move in, 12% had to wait up to a year, and about two-thirds (64%) had to wait for longer than a year, as much as 14 years. For those waiting more than one year, bus provision comes too late to capitalise on the higher likelihood of public transport use through early delivery. To improve public transport delivery in those areas and understand where issues exist, government agencies should monitor the waiting time of communities and support an earlier delivery of public transport through improved land use and transport integration.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".