Synthesizing Population for Agent-Based Microsimulation Modeling in Atlantic Canada
Bibliographic record
Abstract
In the past decade, interests in agent-based microsimulation modeling have increased in the transportation field in response to the growing importance of complex policy measures, including travel demand management and road pricing. Advanced travel demand models use agent-based micro-simulation models to simulate the behaviour of individuals and households rather than aggregate accounting based estimation. The availability of micro-data of population characteristics, the synthesis of individual and household attributes, is necessary for developing a disaggregate, dynamic travel demand forecasting model. In this paper a population is synthesized for individuals and households in Atlantic Canada using the Fitness Based Synthesis (FBS) approach. The synthetic algorithm is examined by three models: using household level control tables (HL model); second, using individual and household level control tables (HPL model); and third, weighting individual and household level control tables (WHPL model). The data used in this study is collected from the 2006 Canadian Census and the 2006 Public Use Micro-data File (PUMF). The algorithm is implemented using a high-level matrix programming language for numerical computation in MATLAB. Validated by error percentages and goodness-of-fit evaluation, FBS can efficiently obtain a satisfactory result using both individual and household level control tables. Furthermore, distribution of selected households in WHPL model is more homogeneous than in HPL model, although the results of HPL also provide a good fitness value. The framework for generating synthetic population presented is a fundamental contribution to the development of an Integrated Transport, Land Use and Environment Modelling System in Nova Scotia, Canada.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".