The Paradox of Public Transport Peak Spreading: Universities and Travel Demand Management
Why this work is in the frame
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Bibliographic record
Abstract
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. Key Words: peak spreadingpublic transporttravel demand managementtravel planning ACKNOWLEDGMENTS This article is based on a project at the Institute of Transport and Logistics Studies funded by the Centre for Transport Planning and Product Development at NSW Department of Transport (formerly NSW Ministry of Transport). We also wish to acknowledge the helpful comments of anonymous reviewers of the article. However, the article and its conclusions are the views of the authors, and not the NSW government. Notes 1Journey to Work data (Mode15) from 2006 Census for Travel Zones 239 and 240. 2Of the 4,992 jobs in the 2 travel zones, 87% are in Education and Training industry sector, and jobs in other industry sectors (eg Accommodation and Food Services, Retail Trade) are assumed to be related to the University. Notes. 1Modes: Private vehicle = car as driver, car as passenger, truck, motorbike; Public transport = train, bus, ferry, tram; Active transport = walk only, bicycle. Based on Mode15, with coding to priority modes. 2University of Sydney data from Table 1; 3CBD and total Sydney data from Transport Data Centre (Citation2008). 1Average of three days (Monday-Wednesday) from a single week in September 2008. 2Student exits includes apprentices but excludes international students. Source: Station exits data from Transport NSW. 1Boardings on corridor up to Ross St stop on Parramatta Rd. 2Boardings on corridor up to Butlin St stop on City Rd. 3Boardings on corridor from Ross St stop on Parramatta Rd towards City. 4Boardings on corridor from Butlin St stop on City Rd towards City. Source: Compiled by Transport NSW from State Transit Authority data.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it