DST-TransitNet: A Dynamic Spatio-Temporal Model for Robust Station-Level Transit Ridership Prediction
Bibliographic record
Abstract
Accurate short-term prediction of station-level transit ridership is crucial for effective transit operations, especially in dynamic urban environments. Traditional methods (e.g., ARIMA, SARIMA) fail to capture intricate spatial-temporal correlations and handle dynamic ridership fluctuations. This study introduces DST-TransitNet, a hybrid deep learning model integrating Graph Attention Networks (GAT) and Gated Recurrent Units (GRU) to model spatial-temporal dependencies among transit stations dynamically. A time-series decomposition framework further improves prediction accuracy and interpretability. Evaluated on Bogotá’s BRT system under normal operations, social disruptions, and pandemic conditions, DST-TransitNet achieved up to 94% R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and reduced prediction errors (MAAPE) by approximately 10%-15% compared to state-of-the-art benchmarks. These results demonstrate DST-TransitNet’s superior accuracy, robustness, and scalability across diverse prediction scenarios and changing time intervals.
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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 | 0.000 | 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".