PUBLIC TRANSPORT CROWDING: THE CURRENT STATE OF FORECASTING TECHNIQUES IN THE UK AND AUSTRALIA
Bibliographic record
Abstract
While congestion on the roads has been part of established modelling and forecasting practice for many decades, public transport crowding has only been incorporated in modelling techniques in Europe for about 20 years and in some parts of the world it is only now being implemented. In London, crowded assignment algorithms have been used in the LTS and RAILPLAN models since 1989. Although some modifications have been made over the years, the underlying techniques have remained unchanged for two decades. However, TfL is now experimenting with capacity constrained assignment methods that represent crowding not only via an in-vehicle time penalty but also through modification of effective frequencies to represent the actual lengthening of headways in crowded conditions. Elsewhere in the UK, crowded assignment is an established part of urban public transport models in most major conurbations, and it is also used in inter-urban rail models such as PLANET. Crowded assignment is also used extensively in the US and Canada, as well as continental Europe. In Australia, by contrast, the practice or crowded public transport assignment modelling is only now being considered seriously in some major conurbations. Recent studies have been undertaken to consider options for the implementation of crowding in the following metropolitan models: the Sydney Strategic Transport Model (STM); the Melbourne Integrated Transport Model (MITM); the Brisbane Strategic Transport Model - Multi Modal (BSTM-MM); the Perth Strategic Transport Evaluation Model (STEM); the Metropolitan Adelaide Strategic Transport Evaluation Model (MASTEM); and the Canberra Strategic Transport Model (CSTM). Of these, only Sydney and Brisbane have taken significant steps towards implementing crowded public transport assignment. In Brisbane, crowded assignment has been implemented in 2010 as part of the model development for the Cross River Rail project. This project is intended to address the current physical constraints for rail travel into the Central Business District, which are estimated to shortly be at capacity. This paper presents a round-up of the current state of forecasting for public transport crowding in the UK and in Australia. It focuses on the differences in approach adopted for different cities and exposes the challenges faced, including data availability, model validation and economic appraisal results.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".