4 citations · 5 across the 4 of their papers we have counts for
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cs.AI2026
EventOD: Event-Aware OD Flow Generation via LLM-Guided Semantic Modulation
Jie Zhao, Jie Feng, Can Rong +3
Estimating origin-destination (OD) flows under disruptive events is important for disaster response and urban resilience. Existing deep OD models trained on routine mobility often…
cs.AI2026
DynaOD: Dynamic Origin-Destination Flow Generation with Discrete-to-Continuous Temporal Semantic Modeling
Jie Zhao, Xianqi Dai, Jie Feng +2
Dynamic origin-destination (OD) flow generation seeks to synthesize realistic mobility dynamics from temporal context alone, without relying on historical OD observations. A key ch…
cs.AI2021★ 1 cited
One-shot Transfer Learning for Population Mapping
Erzhuo Shao, Jie Feng, Yingheng Wang +2
Fine-grained population distribution data is of great importance for many applications, e.g., urban planning, traffic scheduling, epidemic modeling, and risk control. However, due…