Synthesizing Population for Microsimulation-based Integrated Transport Models Using Atlantic Canada Micro-data
Why this work is in the frame
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Bibliographic record
Abstract
Due to the lack of availability of micro-data of population characteristics, the synthesis of individual and household attributes is a necessary step for developing a disaggregate, dynamic travel demand forecasting model. Agent-based micro-simulation models attempt to forecast travel behaviour of individuals and households by simulating the behaviour of the singular actors in the system. The framework for generating synthesis population presented in this paper is a fundamental contribution to the development of an Integrated Transport, Land Use and Environment Modelling System in Nova Scotia, Canada. In this paper, a population is synthesized for individuals and households in Atlantic Canada using the Fitness Based Synthesis (FBS) approach. A synthetic algorithm is designed that allows both individual and household attribute levels to synthesize simultaneously. Unequal probabilities based on the sampling weight are used in the household selection step of the algorithm. In this way, the performance and accuracy of the synthetic population produced has been improved. The synthetic algorithm is tested for two functions: first, using the one level (household) control tables; and second, using two levels (individual and household) control tables. 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. The results show that the synthetic population with both individual and household level attributes has the best fitness value.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it