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Record W2808647260 · doi:10.5198/jtlu.2018.1273

Viewpoint: Integrated urban modeling: Past, present, and future

2018· article· en· W2808647260 on OpenAlexaff
Eric J. Miller

Bibliographic record

VenueJournal of Transport and Land Use · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsExploitState (computer science)AKAUrban planningLand useComputer scienceData scienceRegional scienceManagement scienceOperations researchPolitical scienceEnvironmental planningEngineeringSociologyComputer securityGeographyCivil engineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.286
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2018
Admission routes1
Has abstractyes

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