Temporal transferability of mode-destination models: summary of literature, initial findings
Bibliographic record
Abstract
This paper presents progress to date on the author's PhD research into the temporal transferability of mode-destination models. The motivation for the research effort is that the issue of model transferability is important in the context of transport planning, where forecast horizons of up to 30 years are used. Implicit in such applications is the assumption that travellers tastes, as represented by the model parameters, are constant over time. The research effort seeks to investigate the existing evidence base underlying that assumption, undertake additional empirical analysis to add to the evidence base, and set out recommendations for practitioners to improve practice. The paper will set out exactly what is meant by model transferability, drawing a distinction between temporal and spatial transferability. Measures that can be used to assess model transferability will be detailed, with a discussion of their relative strengths and weaknesses. The next section of the paper will summarise the findings from the literature review (recently published in TRR 2175). The review highlights how most of the evidence dates from early applications of discrete choice transport models in the late 1970s and early 1980s. It also highlights that the evidence is concentrated on mode choice studies applied over short time periods (typically a few years), and that little empirical evidence exists as to the transferability of mode-destination choice models over longer term forecasting horizons. Next, the initial findings from the new empirical analysis will be presented. Toronto Transportation Tomorrow Survey (TTS) data collected in 1986, 1996, 2001 and 2006 will be used to undertake tests of model transferability over transfer periods of up to 20 years. The initial analysis will develop base models from the 1986 TTS data, and assess their transferability over 10, 15 and 20 year horizons using the 1996, 2001 and 2006 TTS datasets, together with supporting level-of-service and land-use data for each year. The analysis will be undertaken using home-work tours. The paper will conclude with findings from the literature review and initial empirical analysis, and then set out the plans for the remainder of the research effort.
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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 | 0.018 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".