Predicting short-Term bike-Sharing demand at station level: A multi-Task dynamic graph-based spatiotemporal approach
Why this work is in the frame
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Bibliographic record
Abstract
Bike-sharing systems face challenges with supply-demand imbalances, causing user dissatisfaction and inefficiency. Accurate prediction of demand is crucial for optimizing these services. While deep learning methods have explored spatiotemporal dynamics in bike-sharing demand, most rely on predefined spatial correlations and focus solely on bike check-out demand, neglecting the relationship with check-in demand. To address these gaps, this paper introduces the Multi-Task Dynamic Graph-based Neural Network (MTDG) for predicting hourly bike-sharing demand across city stations. Initially, we analyze historical data and identify three key historical features for each time interval: closeness, period, and trend. Subsequently, we design three separate streams, each targeting one historical feature, with components to capture spatial and temporal dependencies in each. Spatial information is extracted using a graph convolution operator combined with a time-varying semantic adjacency graph based on historical demand similarities. We employ dual-input Long Short-Term Memory (di-LSTM) recurrent block to learn temporal dependencies and facilitate multi-task learning. This component enables the extraction of hidden pairwise demand correlations by treating the prediction tasks of bike check-in and check-out demands as interrelated. We also incorporate global features, like meteorological data, to capture broader-scale changes. The short-term bike-sharing check-in and check-out demands are jointly predicted by integrating spatiotemporal representations from three streams with global features. Using data from Montreal and New York City’s bike-sharing services, our model outperforms state-of-the-art methods, including fully adaptive graph models and Large Language Models (LLM)-based forecasting models. Variants of MTDG, such as single-task methods and alternative semantic adjacency graph configurations, also show superior performance over most baseline models.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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