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 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 | 0.001 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".