Activity Location Assignment Comparison Using Geospatial Landuse and Building Data in MATSim : A Multi-modal Transport Case Study of Stockholm
Bibliographic record
Abstract
Transport simulation models play a crucial role in transportation planning, design, and operations, allowing for the replication of various scenarios through the incorporation of real-world data and parameters. Recently, agent-based transport models have gained prominence for their ability to simulate intricate metropolitan transport systems. These models take into account the distinct characteristics, decision-making processes, and interactions of individual agents. Among the array of agent-based transport models, MATSim stands out as a potent and adaptable tool for modeling transportation systems. A critical aspect of MATSim’s input preparation involves assigning activity location points using land use raster data. However, the characteristics of land use raster data present limitations in certain urban case studies such as Stockholm. In response, some researchers have turned their attention to buildings shapefile data, a commonly used geospatial data format. This study aims to improve the activity location assignment model by developing an evaluation workflow of model uncertainty for different geospatial input data in MATSim and empirically analyzing their impacts on simulation outcomes. Despite acknowledging data availability and activity representation limitations, the study’s results demonstrate that utilizingbuildings shapefiles as input data yields more consistent outcomes with reduced uncertainty. This suggests the promising potential of buildings shapefiles as a favorable data source for transportation modeling and planning within the studied scenarios.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".