3 papers
cs.LG2026
Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand
Yihong Tang, Tong Nie, Junlin He +3
Forecasting urban delivery demand becomes substantially more challenging when newly added service regions lack historical records. Existing spatiotemporal forecasters effectively m…
cs.LG2025
Error Adjustment Based on Spatiotemporal Correlation Fusion for Traffic Forecasting
Fuqiang Liu, Weiping Ding, Luis Miranda-Moreno +1
Deep neural networks (DNNs) play a significant role in an increasing body of research on traffic forecasting due to their effectively capturing spatiotemporal patterns embedded in…
cs.LG2025
Robust Tensor Completion via Gradient Tensor Nulclear L1-L2 Norm for Traffic Data Recovery
Hao Shu, Jicheng Li, Tianyv Lei +1
In real-world scenarios, spatiotemporal traffic data frequently experiences dual degradation from missing values and noise caused by sensor malfunctions and communication failures.…