Demographic Microsimulation Model for Integrated Land Use, Transportation, and Environment Model System
Bibliographic record
Abstract
The Integrated Land Use, Transportation, Environment (ILUTE) model system is an agent-based microsimulation model that dynamically evolves the urban spatial form, economic structure, demographics and travel behavior over time for the Greater Toronto-Hamilton Area (GTHA). This paper presents the ILUTE Demographic Updating Module (I-DUM), which updates the residential population demographics throughout the simulation. Given a synthetic base population, I-DUM updates the attributes of the agents at each time step. New agents are introduced through birth and in-migration, while agents exit through death and out-migration events. Unions between agents are formed through a marriage market, while a divorce model dissolves existing ones. Transitions to new households are also triggered by a move-out model. In addition to its comprehensive scope, I-DUM is a closed demographic model where social networks are built and maintained throughout the simulation. Maintaining social connections brings some advantages for modeling travel behavior and location choice. I-DUM is being tested against a twenty-year (1986-2006) period using a 100% synthetic GTHA population (4.1 million persons, 1.1 million families, 1.4 million households). The results are compared against historical observations across multiple dimensions. In general, I-DUM exhibits a strong performance, and the authors have confidence that it can maintain the validity of inputs to the other behavioral models in ILUTE. I-DUM's implementation has also been parallelized which brings significant performance improvements. Starting with over 6.5 million agents (which grows past 10 million), the simulation takes just under 10 minutes to complete a twenty-year run.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".