Counting the different efficient paths for transportation networks and its applications
Bibliographic record
Abstract
This paper deals with an interesting problem about how to efficiently compute the number of different efficient paths between an origin-destination pair for a transportation network because these efficient paths are the possible paths used by drivers to some extent. Based on a novel triangle operation derived, it first presents a polynomial-time combinatorial algorithm that can obtain the number of different simple paths between any two nodes for an acyclic network as well as the total travel cost of these paths. This paper proceeds to develop a combinatorial algorithm with polynomial-time complexity for both counting the different efficient paths between an origin-destination pair and calculating the total travel cost of these paths. As for applications, this paper shows that the preceding two algorithms can yield the lower and upper bounds for the number of different simple paths between an origin-destination pair, while it has already be recognized that a polynomial-time algorithm getting such a number does not exist for a general network. Furthermore, the latter algorithm can be applied for developing a heuristic method for the traffic counting location problem arising from the origin-destination matrix estimation problems.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".