Bibliographic record
Abstract
Integrated urban models (IUMs) (aka, integrated transport/land-use models) have been developed and (sometimes) applied for more than 50 years, dating back to the early 1960s. IUMs have been criticized over this same period on both practical and theoretical grounds. At the same time, continuing and very significant technological developments have made possible the development, implementation and use of such models in operational planning settings in various countries worldwide. A major review of the IUM state of the art and recommendations for evolution of this state were prepared by the author and colleagues 20 years ago. This paper presents an update of the 1998 report in terms of a summary of progress over the past 20 years, a critical assessment of the current IUM state of the art and practice, and needs and prospects for future development. This paper argues that the current modeling state is in “the doldrums,” similar to concerns raised by Pas in the seminal 1990 critique of activity-based travel models. It then outlines research and development needs to exploit current and emerging data, computing, and methodological developments that hold promise for the development of a much more powerful and useful “next generation” of IUMs.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| 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".