Multivariate event hypergraph diffusion model for train delay prediction
Bibliographic record
Abstract
Train delay prediction is a key technology for train scheduling and timetable optimization, and constitutes a critical component of intelligent transportation systems. We present the first study on regional-level multi-train delay prediction problem, and focus on modeling the regional-level delay propagation and evolution process, and capturing coordinated operation status among multiple train clusters in the complex operation network. First, we propose a brand-new Multivariate Event Hypergraph Diffusion (MEHD) model, and introduce a novel data structure, the mixed hypergraph, which accurately models the spatio-temporal high-order correlations between the regional-level multi-train arrival events. Then, we propose a mixed hypergraph convolution method to characterize complex train operation network, which improves the ability to capture the spatio-temporal high-order correlations and non-Euclidean characteristics between events. Finally, we propose an event hypergraph diffusion process, and design a prior operational schedule-conditioned attention denoising module to enhance the ability to learn all train arrival event generation mechanisms. Extensive experiments demonstrate that our MEHD achieves superior performance compared to current state-of-the-art models on actual high-speed rail performance datasets, with an average improvement of 20%-30% on multiple metrics, and performs good robustness and efficiency. Subsequent experiments and analyses demonstrate the unique advantages of MEHD over single-train prediction methods. To the best of our knowledge, this is the first end-to-end model for regional-level multi-train delay prediction. The dataset and source code are available online: https://github.com/bjtuxuyi/MEHD .
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".