An Efficient Shortest Distance Decomposition Algorithm for Large-Scale Transportation Network Problems
Bibliographic record
Abstract
For numerous large-scale engineering and science problems, domain decomposition (DD) has generally been accepted by research communities as among the most attractive methods to obtain solutions efficiently. As a pre-requisite for the DD solution process, a large domain must be partitioned into several smaller sub-domains, with the key to success (of any DD partitioning algorithm) being the number of system boundary nodes. The lower this number, the more efficient the sub-domains can be processed in parallel. Although various transportation researchers have hinted at the use of DD, for example, in decentralized traffic management, it is always assumed the partition is provided. This paper presents a simple, efficient and effective algorithm to decompose a transportation network into a predefined number of inter-connected sub-domains such that the number of system boundary nodes is small (first priority) and the number of nodes in each sub-domain is similar (second priority). This algorithm was based on a simplified version of the Polynomial (partitioned) Label Correcting Algorithm (P-LCA), rather than the classical (Bellman-Ford) LCA, coupled with simple heuristic rules. To assess the effectiveness (in terms of minimizing the number of system boundary nodes) of the proposed Shortest Distance Decomposition Algorithm (SDDA), it is compared with METIS, which is currently the most widely used graph partitioning algorithm world-wide. Using large-scale, real-world transportation test networks, it was found the SDDA is significantly better than METIS; the SDDA outperformed METIS in 23 of 27 examples, and on average provided (approximately) 46% fewer boundary nodes for large-scale examples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.002 | 0.001 |
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".