A Multi-Source Dynamic Temporal Point Process Model for Train Delay Prediction
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Bibliographic record
Abstract
Train delay prediction is a key technology for intelligent train scheduling and passenger services. We propose a train delay prediction model that takes into account the asynchrony of train events, the dynamics of train operations, and the diversity of influencing factors. Firstly, we consider train operations as discrete sequences of train events and propose a train arrival neural temporal point process (TANTPP) framework focused on predicting train delays that explicitly models the asynchrony of train events. Secondly, we introduce a multi-source dynamic spatiotemporal embedding method for the feature encoder in the TANTPP framework, which enhances the capability to capture the features of train operation networks. Thirdly, to better capture the distribution of train events in the TANTPP framework, we utilize a log-normal mixture hybrid method to learn the probability density distribution of train arrival events. Finally, the experimental result on real-world datasets demonstrates that the TANTPP model outperforms current state-of-the-art models, reducing the MAE by 10.85%, the RMSE by 9.8%, the RRSE by 3.78% and the MAPE by 10.11% on average. To the best of our knowledge, this is the first study to utilize neural temporal point processes to enhance train delay prediction.
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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) | 0.000 | 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) | 0.000 | 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