Building the Case: Improving the Credibility and Reliability of Travel Demand Models to Meet Changing Needs
Bibliographic record
Abstract
For over half a century, travel demand models have been used to project travellers' behaviour under different circumstances such as demographic and economic growth, network improvements and policy changes. The resulting forecasts have been used as the basis for road, transit and other infrastructure investment decisions, so much relies on getting the forecasts 'right.' The paper discusses the changing roles for models and their forecasts in the context of new planning requirements (e.g., sustainability and financing); who needs information and what kind of information they need from the models; the importance of improving model reliability and credibility; and the techniques and data for making these improvements. The paper draws from experience in Canada and the United States, where local and state authorities have had to start positioning themselves to accommodate alternative financing (such as P3) and address new initiatives (such as TDM). The paper will be of interest to Canadian government agencies at all levels that are charged with making investment decisions to meet growing transportation demand and emerging mobility needs. For the covering abstract of this conference see ITRD number 201211RT334E.
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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 | 0.071 | 0.278 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".