Population Synthesis for Microsimulating Urban Residential Mobility
Bibliographic record
Abstract
In recent years, transportation researchers have turned to microsimulation models in order to capture and forecast patterns of land use and transport demand across urban networks. Although they bring with them many advantages, microsimulation models require rich micro-data sets as input. In response to this demand, and due to the difficulty of obtaining disaggregate data in practice, methods of population synthesis are employed, which create disaggregate population lists based on publicly available attribute tabulations and micro-samples. A further challenge arises when microsimulation models require that hierarchical relationships exist between different populations in a given study area. For instance, in microsimulation models of residential mobility a ‘comprehensive’ population may be required, where individuals belong to households, which in turn belong to dwellings located in space. It is to such a situation that the authors focus their efforts in this paper, based on the requirements of a specific microsimulation model of residential mobility. Following a brief discussion of the problem in general terms, the authors devise and implement an algorithm in order to create linkages between populations of individuals and households for the City of Hamilton, Ontario. For this task, software is developed in the Python language, using a generic approach that allows for its use in other similar situations. Finally, the authors report on some validation results of the linked ‘comprehensive’ population.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".