Predicting short-Term bike-Sharing demand at station level: A multi-Task dynamic graph-based spatiotemporal approach
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".