Associations Generation in Synthetic Population for Transportation Applications
Bibliographic record
Abstract
The generation of synthetic populations through simulation methods is an important research topic and has a key application in agent-based modeling of transport and land use. The next step in this research area is the generation of complete synthetic households; this research area requires some way to associate synthetic persons with household positions. This work formulated the person to the position matching problem as a bipartite graph matching and tested two models for determining match utility with data from the 2000 Swiss census. The functions tested were both multinomial logit models, one based on the household size attribute and the other on household type. Synthetic persons were matched into the head position of real households, and then the remaining population was used to run a second match with a separately calibrated version of the size choice model for the spouse position. This method is a long list-based approach that keeps the original marginal consistent. Results show that the size choice model returns the best results for head and spouse positions, although both models provide a good match quality as measured by the distributions of individual attributes in real and matched populations as well as the distributions of unique attribute combinations. Possible extensions include matching to other household positions and evaluating the performance of these synthetic households in modeling applications.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".