Rail transit OD‐matrix completion via manifold regularized tensor factorisation
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Bibliographic record
Abstract
Abstract Urban rail transit has become an indispensable mode in major cities worldwide regarding the advantages of large capacity, high speed, punctuality, and environmental protection. Origin‐destination (OD) matrix data is crucial to the organisation of rail train operation and management. Nevertheless, rail transit OD matrices are inevitably suffered from data loss problems due to the data transmission and acquisition failures. Tensor completion is a state‐of‐the‐art method for missing data imputation. In this paper, a novel tensor completion method for OD‐ matrix completion is proposed. To this end, an OD‐matrix tensor is established to represent OD information, and the similarity matrix of OD‐matrix tensor for each dimension is extracted as a piece of auxiliary information expressing underlying multi‐mode relationships of OD data. Finally, a manifold regularised tensor factorisation is applied to impute the missing OD data, in which the Graph Laplacians inferred from similarity weight matrices are used as regularisation priors on factorisation factors. The proposed model is applied to a case study of the metro line in Xi'an, China. The experimental results indicate that the proposed method outperforms baselines. It can accurately impute missing data within the OD matrices and work well even when the missing ratio is up to 80%.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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