1 citations · 1 across the 3 of their papers we have counts for
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★ 1 cited
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.…