The Paradox of Public Transport Peak Spreading: Universities and Travel Demand Management
Bibliographic record
Abstract
The characteristics which make public transport attractive and contribute to high public transport use by specific market segments create the paradox in which encouragement of peak spreading of public transport services may lead to lower overall use of public transport. As an example of this potential paradox, the challenges of spreading peak demand for public transport for a large inner city trip generator, the University of Sydney in inner Sydney NSW, Australia are investigated, from both the demand side and supply side. While there is a range of university and government initiatives which would reduce peak use and encourage peak spreading such as class scheduling, provision of student housing, travel planning, and changes to public transport supply and pricing, they may not achieve either a reduction in peak use or a spread of public transport demand to other times of the day. Education users are the most dedicated users of public transport and, for a peak spreading campaign to be successful, finely balanced messages are required to encourage peak public transport users such as students to shift to the off-peak, and for peak car drivers such as staff not to replace these users on peak public transport services.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".