Development of an Integrated Transportation and Land Use Microsimulation Model on a Flexible Modeling Platform
Bibliographic record
Abstract
This paper reports the development of a microsimulation model-building platform and the implementation, validation, and deployment of an integrated transportation and land use model using the platform. The modeling platform provides a new approach to microsimulation by providing modular algorithms with which models can be configured without additional coding, enabling the rapid development, testing, and deployment of models. The integrated transportation, land use, and energy (iTLE) model is an operational microsimulation model for Halifax, Canada, that combines long-term household decisions with short-term travel choices. The model’s longer-term decision simulator simulates life-stage transitions, residential mobility, and vehicle transactions. The residential mobility module uses a logsum measure of modal accessibility to capture how households’ travel options influence their location decisions. The model was run from 2006 to 2036 on a business-as-usual scenario of land use and transportation. Validation performed after 10 years against census data indicates that the model produces a well-calibrated simulated population. The model predicts rising population density and greater household incomes in the urban core and a rise in vehicle ownership. The disaggregate nature, integration of land use and travel behavior, and life-course perspective of the modeling framework make it valuable as a decision-support tool for testing alternate scenarios against the baseline. The ease of development afforded by the modeling platform will facilitate future research on new mobility options, ensuring that the model can easily be adapted to anticipated transformational changes in the transportation system.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".