A solution to the road network design problem for multimodal flow
Bibliographic record
Abstract
Given a set of candidate road projects associated with costs, finding the best subset with respect to a limited budget is known as the discrete network design problem (DNDP). The DNDP is often cast in a bilevel programming problem which is known to be NP-hard. Despite a plethora of research, due to the combinatorial complexity, efforts to address the problem for large-sized networks while considering public transport as well as other modes (multimodal/multiclass traffic flow) are scarce. To this end, we first turn the bilevel problem to a single-level problem based on System-Optimal traffic flow which results in a mixed integer non-linear programming (MINLP) problem. Second, we develop an efficient Benders decomposition algorithm to solve the ensuing MINLP problem. The multiclass/multimodal features of the traffic flow are ensured by employing the Spiess' bias term and optimal strategy methods. The proposed methodology is applied to Sioux Falls and a real sized network of the city of Winnipeg, Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".