Bus passenger flow prediction with a dynamic graph neural network and bidirectional gated recurrent unit
Bibliographic Data
| ID | 24019408 |
|---|---|
| Authors | Xinyi Zhou (0000-0002-2388-254X, Concordia University, corresponding author), Nizar Bouguila (0000-0001-7224-7940, Concordia University), Zachary Patterson (0009-0006-0804-2688, Concordia University) |
| Year | 2026 |
| Volume | 149 |
| Pages | 107766 |
| Publication date | 2026-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sustainable Cities and Society (JOURNAL) |
| Journal identifiers | ISSN: 2210-6707 • E-ISSN: 2210-6715 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.scs.2026.107766 |
| OpenAlex | W7172432365 |
| Language | EN |
| References cited | 53 |
Artificial neural network · Control flow graph · Control theory (sociology) · Flow (mathematics) · Graph · Unit (ring theory) · Traffic control and management · Traffic Prediction and Management Techniques · Transportation Planning and Optimization
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A hybrid spatial learning method integrating graph neural networks and explainable AI to understand urban vitality
Generating sparse origin–destination flows on shared mobility networks using probabilistic graph neural networks
Comparative analysis of deep-learning-based models for hourly bus passenger flow forecasting
| Citation velocity | historical |
|---|---|
| Highly cited | No |