Implementation of a "Next Generation" Activity-Based Travel Demand Model: The Toronto Case
Bibliographic record
Abstract
Disaggregate, activity-based travel demand models have been promoted for several decades as being more behaviourally sound and, as a result, more policy sensitive demand forecasting tools than conventional aggregate, trip-based, “four-step†procedures. In the past decade numerous operational models have been implemented in a number of US metropolitan regions as well as in Europe. This paper discusses the development and implementation within operational planning practice of the first fully activity-based travel demand model in Canada, the GTAModel V4.0 model system for the Greater Toronto-Hamilton Area (GTHA). Based on over a decade of research at the University of Toronto, “V4.0†has recently been used by the City of Toronto in a major study of transit infrastructure investment strategies. It has also been adopted by the City of Mississauga for use in future planning studies. The paper discusses the advantages of the disaggregate activity-based approach to travel demand modelling. It then provides a concise overview of the key features and procedures of the V4.0 model system, as well as a detailed bibliography of more detailed documentation of the model system. The paper then discusses the operational implementation of the model system and briefly describes the on-going first application of the implemented model system in the analysis of major rail transit investment alternatives for the City of Toronto. The paper concludes with a few “lessons learned†that may be useful for other Canadian urban regions considering the evolution to activity-based model system formulations for their operational use.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".