IMPACTS OF SETTLEMENT PATTERNS AND DYNAMICS ON URBAN MOBILITY BEHAVIOUR: FINDINGS FROM THE ANALYSIS OF MULTIPLE DATA SOURCES
Bibliographic record
Abstract
This paper describes how there is a widespread recognition that transportation and land-use are strongly related. Actually, an extensive literature documents our current understanding of relationships linking urban form factors (residential and employment density, transit supply, auto ownership, accessibility and socio-economic factors such as income, age, gender and occupation) with travel activity (travel distances, modal split, mobility rate). Worth mention is a review of literature conducted in 1998 that summarized the current understanding of the implications of land-use on transit and the implications of transit on urban form in terms of influent factors. Noted in this research is the fact that while transportation and land-use are strongly related, the current means of analyzing this relationship are limited. Urban sprawl, when observed according to its time dynamics, generates strong structural changes in travel behavior for commuters. For metropolitan transportation planners, recent and urgent concerns are emphasizing needs for clarifying the mutual impacts between land-use and transportation networks. In the same context, transport systems analysis at the metropolitan level faces the methodological challenge of accessing, structuring and exploiting relevant information from multiple data sources. This paper defines an analytical framework for modeling the impacts of settlement patterns and related mobility behavior by the incorporation of multi-dimensional variables in order to represent the complexity of the urban process phenomena. The question of forecasting future settlement pattern and related mobility is also addressed. An extensive experimentation with the Montreal data constitutes a demonstration of the applied methodology.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".