An Offline Road Network Partitioning Solution in Distributed Transportation Simulation
Bibliographic record
Abstract
Offline road network partitioning is the first step to space-parallel distributed transportation simulation. Currently, METIS is the most popular offline road network partitioning solution, but it cannot naturally formalize data distribution in various ITS applications, and cannot guarantee to minimize data exchanges between partitions. This paper introduces a hyper graph-based offline road network partitioning solution, which is suitable for future distributed transportation simulations with ITS applications. In [10], we proposed to formalize offline road network partitioning as a hyper graph partitioning problem, which makes it possible to minimize data exchanges between partitions. We then solved the hyper graph partitioning problem using hMETIS, a graph partitioning algorithm borrowed from Very Large Scale Integration (VLSI) applications. In this paper, our experiments based on Singapore road network showed that the hyper graph-based road network partitioning with ITS applications reduces data exchanges between partitions. We observed two features in data distributions in some ITS applications, which led us to develop the biased first choice (BFC) coarsening schema. Experiments show that BFC further reduces data exchanges between partitions. For distributed transportation simulations, where there are large amounts of data exchanged between partitions, especially by ITS applications, our proposal is one candidate solution to reduce the simulation time and increase the scalability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".