Zhenji Gao
Biographic Data
| ID | 9966021 |
|---|---|
| NAME | Zhenji Gao |
| GIVEN NAMES | Zhenji |
| FAMILY NAME | Gao |
| SIGNATURE | GAO Z |
| AFFILIATIONS | Ministry of Natural Resources |
| ORCID | 0009-0007-9106-039X |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Urban Grid Population Inflow Prediction via POI-Enhanced Conditional Diffusion with Dual-Dimensional Attention
Accurate prediction of urban grid-scale population inflow is crucial for smart city management and emergency response. However, existing methods struggle to model spatial heterogeneity and quantify prediction uncertainty, limiting their accuracy and decision-support capabilities. This paper proposes PDCDM (POI-enhanced Dual-Dimensional Conditional Diffusion Model), which integrates urban functional semantic awareness with conditional diffusion mo…
Cross-Attention Diffusion Model for Semantic-Aware Short-Term Urban OD Flow Prediction
Origin–destination (OD) flow prediction is fundamental to intelligent transportation systems, yet existing diffusion-based models face two critical limitations. First, they inadequately exploit spatial semantics, focusing primarily on temporal dependencies or topological correlations while neglecting urban functional heterogeneity encoded in Points of Interest (POIs). Second, static embedding fusion cannot dynamically capture semantic importance …
No prominent works on this page.
Urban Grid Population Inflow Prediction via POI-Enhanced Conditional Diffusion with Dual-Dimensional Attention
Accurate prediction of urban grid-scale population inflow is crucial for smart city management and emergency response. However, existing methods struggle to model spatial heterogeneity and quantify prediction uncertainty, limiting their accuracy and decision-support capabilities. This paper proposes PDCDM (POI-enhanced Dual-Dimensional Conditional Diffusion Model), which integrates urban functional semantic awareness with conditional diffusion mo…
Cross-Attention Diffusion Model for Semantic-Aware Short-Term Urban OD Flow Prediction
Origin–destination (OD) flow prediction is fundamental to intelligent transportation systems, yet existing diffusion-based models face two critical limitations. First, they inadequately exploit spatial semantics, focusing primarily on temporal dependencies or topological correlations while neglecting urban functional heterogeneity encoded in Points of Interest (POIs). Second, static embedding fusion cannot dynamically capture semantic importance …
Human Mobility and Location-Based Analysis (2 works) · Traffic Prediction and Management Techniques (2 works) · Conditional probability (1 work) · Diffusion (1 work) · Embedding (1 work) · Exploit (1 work) · Grid (1 work) · Inflow (1 work) · Intelligent transportation system (1 work) · Population (1 work)