Railway booking demand forecasting for revenue management: a deep probabilistic approach
Bibliographic record
Abstract
Accurate demand forecasting is vital for effective revenue management in the railway industry. While previous research has mainly concentrated on predicting overall ridership, the prediction of demand during the booking process, which is a more pertinent issue in revenue management, has not received sufficient attention. This paper bridges this significant gap by introducing a probabilistic railway booking demand forecasting model – Deep AutoRegression with Cross-itinerary Attention (DARCA). DARCA comprises an embedding layer that encodes itinerary-specific information, a Long Short-Term Memory (LSTM) network that captures temporal correlations from past booking demand, a Transformer encoder that models interdependencies among related itineraries, and an output layer leveraging the negative binomial distribution for probabilistic forecasting. Unlike traditional approaches that use many models for different markets and forecasting horizons, DARCA is a single model that addresses the railway booking demand forecasting at any horizon and aggregation level, significantly simplifying the forecasting pipeline. With extensive experiments on a large-scale real-world railway booking dataset, results demonstrate that DARCA excels in both point forecast accuracy and probabilistic forecast accuracy across various aggregation levels and forecasting horizons. The success of DARCA highlights the immense capacity of deep-learning-based forecasting models to improve railway revenue management strategies.
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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 | 0.001 | 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".