Bike-sharing ridership prediction for network expansion using graph neural networks
Bibliographic record
Abstract
Ridership prediction in station-based bike-sharing services improves station planning, fleet management, and network design. Ridership inflow and outflow prediction at the station level has received significant attention through trip production and attraction models. However, station-to-station ridership has been studied less, despite its widespread applications in use cases such as bike-lane planning or fleet electrification. This study introduces a Graph Neural Network (GNN) to model station-to-station ridership using a customized Graph Sample and Aggregate framework to generate node embeddings and minimize the weighted Mean Squared Error for peak periods. The model incorporates the characteristics of the network, sociodemographic features, and station properties. We present the case study of Bikeshare Toronto to train and test the GNN model and benchmark it against other standard prediction methods. We show that the GNN outperforms linear regression, spatial regression, XGBoost, and artificial neural networks due to its ability to capture the impact of the network structure on ridership patterns. We incorporate the GNN model in five design scenarios focusing on urban core connectivity, suburban access, transit integration, equitable accessibility, and tourist hubs. Each scenario is strategically developed to prioritize and address unique urban challenges. To enhance the model’s application in real-world planning, we embedded the model in a web-based tool for the Cities of Vancouver and Toronto, allowing for further “what-if” scenario analysis in bike-sharing network planning.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".