Offline Road Network Partitioning in Distributed Transportation Simulation
Bibliographic record
Abstract
Distributed transportation simulation is an important technology for evaluating various Intelligent Transportation Systems (ITS) applications, before they are implemented in the real-world traffic system. Offline road network partitioning is the first step towards distributed transportation simulation. However, as the most popular offline road network partitioning solution, METIS cannot naturally formalize data distribution in ITS applications, and thus cannot guarantee to minimize data exchanges between partitions. In this paper, we propose to formalize offline road network partitioning as a hyper graph partitioning, which can naturally deal with data distribution in ITS applications. Then, we propose to solve the hyper graph partitioning using hMETIS, a graph partitioning algorithm borrowed from VLSI applications. Preliminary experiments show that even in the case where there is no ITS application, hyper graph-based offline road network partitioning can reduce data exchanges between partitions by around 10% on average. Currently, we are evaluating hyper graph-based offline road network partitioning on ITS applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".