Modeling transport networks with design pattern: application to hybrid traffic simulations
Bibliographic record
Abstract
Being able to vary the level of detail or scale when modeling any system has an increasing interest in different domains. Here we address the issue of multiscale modeling of transport networks in order to enhance feasibility of hybrid simulations, like those who couple macro and micro traffic behaviours, or those who recently tried to combine cellular automata with multi-agent systems in urban simulation and geosimulations. Using an example, we show how a generic link/node representation which forms the core of a design pattern, can be used to instantiate several network models at different scales. Each one can be simulated using the appropriate behavioural model. The design pattern approach avoids drawbacks of strictly hierarchical representations and maintains coherency. We use a multi-level spatial grid to locate vertices that form a link. This hierarchical grid is also a way to deal with behavioural models based on cellular automata. The concept of Place is introduced in order to be able to connect generated synthetic populations to the transport network and, then, to model the travel demand. Multimodality is allowed and opportunities of modal transfers are explicitly defined. The paper also shows how we are using real GIS data of Quebec City to build a three-scale transport network with the suggested approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".