A note on logit choices in strategy transit assignment
Bibliographic record
Abstract
Since it was first developed [see Spiess and Florian, Transp Res 23:83–102 (1989)], the strategy-based transit assignment has been extensively used and its properties are well understood now. The computation of an optimal strategy is relatively fast and is comparable to the computation of a shortest path tree for one destination. However, since it is the solution of a linear program, it produces extremal solutions. As a consequence, the sensitivity analysis of strategy flows is not smooth. This work parallels the contribution of Nguyen et al. (Transp Sci 32:54–64 1998) who developed a logit choice of strategies following a basic idea due to Dial (Transp Res 5:88–111, 1971), in order to consider a larger variety of strategies by allowing walk choices at nodes of the transit network. Nevertheless, since the network representation used is different from the one used by Nguyen et al. (Transp Sci 32:54–64, 1998), the development is different. This modified logit strategy transit assignment algorithm was shown to produce more realistic results in dense transit networks where relatively short walks are required for access to attractive alternative transit paths. It also models better access from centroids representing large zones.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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".