Data-driven predictive model for dynamic expected travel time estimation in rail freight networks: A case study
Bibliographic record
Abstract
Rail freight is vital for economic growth due to its efficiency and environmental benefits, but its lack of fixed schedules due to various delay factors poses challenges for accurate Expected Travel Time (ETT) predictions. This research addresses the need for real-time, accurate and dynamic ETT predictions crucial for maintaining efficient supply chains by developing a novel predictive model that leverages real-time data. The model ensembles Graph Convolutional Network-Long Short-Term Memory (GCN-LSTM) and Kalman Filters (KF) models to capture the complex spatiotemporal interactions and diverse traction behaviours within the freight train railway network. The methodology comprises three phases: modeling, schedule generation, and dynamic updating. In the modeling phase, historical train movement data is used to develop predictive models, with KF handling state-space representation and GCN-LSTM capturing spatial and temporal dependencies. These models are ensembled to enhance prediction accuracy. The schedule generation phase estimates travel times using the ensembled model, the dynamic updating phase refines predictions using real-time data, while congestion is assessed by clustering congested areas with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and propagating these clusters through KF. The proposed model is compared with different state-of-art predictive models. The methodology’s effectiveness was validated using real-world data from Indian Railway freight operations. The proposed model demonstrated superior accuracy, with Mean Absolute Percentage Error of 19.51%, while the moving average-based model which was previously being used by the Indian Railway had an error of 44.34%. This approach, implemented as a decision support system for Indian Railways’ daily operations, provides advanced planning solutions to manage the growing complexities of rail freight logistics effectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".