Theory-informed and interpretable graph learning for urban commuting flows

Abstract

PIG-GNN combines graph learning with spatial interaction theory to model commuting flows. It distinguishes origins from destinations and incorporates geographic scaling constraints, supporting predictions that can be interpreted in relation to established mobility principles.

Cai Wu

Assistant Professor

I study computational urban morphology, GeoAI, and data-driven urban design, developing reproducible methods for understanding cities across scales.