- Quickly forecasting the future state of urban sensors by the missing-data-tolerant deep learning approach. Dans: Sustainable Cities and Society, v. 118 (janvier 2025). (2025):
- Predicting origin-destination flows by considering heterogeneous mobility patterns. Dans: Sustainable Cities and Society, v. 118 (janvier 2025). (2025):
- A deep marked graph process model for citywide traffic congestion forecasting. Dans: Computer-Aided Civil and Infrastructure Engineering, v. 39, n. 8 (janvier 2024). (2024):
- A lightweight spatiotemporal graph dilated convolutional network for urban sensor state prediction. Dans: Sustainable Cities and Society, v. 101 (février 2024). (2024):