The 'disruption' we really need: public transport for the urban millennium
Bibliographic record
Abstract
Mass transit is the only form of motorised transport that can move large numbers of people to the same destination at the same time without either alienating the space needed for social and economic interaction or allowing cities to encroach further upon vital natural environments or agricultural land. Despite the rapid development and deployment of `disruptive' technologies in urban transport, mass transit will still have a vital role to play in the transport systems of the world's great urban regions in coming decades. This is largely because cities of the future will face increasing competition over space. No urban region has been entirely successful in creating mass transit networks that offer speed and convenience approaching that of the private car, but some have done much better than others. This chapter presents case studies of relative success in the creation of space-efficient transport systems in the urban regions of Vienna, Zurich and Vancouver. It gives an overview of transport system performance including operating costs, infrastructure investments and mode share, together with contextual demographic data. In each case, this is accompanied by a short outline of the political and institutional processes that have enabled these outcomes to be achieved. Common features in all the three cities include consistent and skilful engagement in local political processes by transit advocates and planners and coherent use of transport planning practices that give primacy to meet the needs of mass transit users at the lowest possible cost.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".