Data-driven recalibration methodology for spatially transferring the vehicle ownership module of an agent-based integrated urban model
Bibliographic record
Abstract
STELARS is a large-scale Integrated Urban Model that simulates land use, vehicle ownership, and transportation decisions within an agent-based framework. Vehicle ownership in STELARS is conceptualized as a two-stage process involving vehicle transaction and type choice, represented by six interrelated micro-models. Traditional model updating approaches in enhancing transferability have largely focused on recalibrating a single model at a time and have not been designed to handle large, interdependent systems composed of multiple micro-models. This limitation hinders the spatial transferability of such complex frameworks when applied to new geographic regions with different socioeconomic and behavioural characteristics. To address this challenge, this study proposes a data-driven recalibration framework that enables updating of all micro-models within the vehicle ownership module, thereby enhancing spatial transferability while preserving structural consistency. The proposed framework employs a Steady-State Elitist Genetic Algorithm assisted by a Random Forest surrogate model to determine recalibrated parameter sets. For the recalibration, initial parameter sets were generated using Latin Hypercube Sampling within ±100 percent of original calibrated values, and Mean Absolute Error was used as the objective function to minimize. The recalibration was conducted to transfer the vehicle ownership module from the Okanagan Region to the Greater Vancouver Area, British Columbia, utilizing aggregate data sources. The proposed strategy yielded promising results: the simulated household vehicle ownership distribution in the Greater Vancouver Area matched the observed values within a 4 percent discrepancy range. Additionally, 62 percent of the parameters in the vehicle type models after recalibration remained within the ±75 percent range of their original estimates, indicating that most behavioural patterns observed in the Okanagan model were largely retained in the transferred context. Where deviations did occur, they reflected behavioural and contextual differences between the regions. This stability across regions demonstrates the robustness of the original model specification and suggests that while regional differences exist, the overall behavioural structure of vehicle ownership decisions remains transferable. Overall, the proposed framework provides a scalable, resource-efficient, and data-driven approach for transferring complex urban models to data-scarce contexts, thereby broadening their applicability for policy and planning analyses.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".