A Prompt Tuning Approach Based on Pre-trained Cross-City Graph Neural Networks for Zero-Shot Flow Generation

We introduce a generalizable flow generation modeling approach that leverages an efficient pre-trained mobility model to learn generic mobility prior knowledge across different source cities and a light-weight graph prompt learning scheme to fine tune the pre-trained model for flow generation. We provide an implementation using the U.S. COVID-19 mobility flow dataset as an example.