A methodology for schedule‐based paths recommendation in multimodal public transportation networks
Why this work is in the frame
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Bibliographic record
Abstract
SUMMARY This paper analyzes the problem of intermodal itineraries recommendation in interurban networks where different public transportation modes, several companies, time, and capacity constraints, as well as seat booking, are considered. The inherent network optimization problem is first modeled for a generic user request, and then a solving method that makes use of a network graph transformation is proposed. For each request, this solving method is based on pruning the user‐specific time–space graph, followed by the application of a k ‐shortest path algorithm. Moreover, in order to build on‐demand real‐time itineraries recommendations, the algorithm has been embedded in a Web client–server to which users ask for trip recommendations by Internet or mobile phone. Finally, as an illustration, the proposed approach has been tested on the Andalusia main transportation network. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.001 | 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 | 0.004 |
| 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 it