TRANSFERRING ACTIVITY PROTOTYPES FOR ZERO- SHOT CROSS-CITY MOBILITY GENERATION

preserving privacy. It has become increasingly important for urban planning and management in smart cities. However, training a generative model requires abundant local real trajectory data, which many cities cannot provide. An effective alternative is to transfer behavioral knowledge from data-rich cities. Nevertheless, differences in location semantics and spatial structure hinder the direct reuse of source individuals’ activity patterns. To address this challenge, we propose SHAPE, a cross-city mobility generation framework that extracts shared activity patterns as core representations for cross-city transfer. The key idea of SHAPE is to encode individual activity patterns into interpretable, city-independent activity prototypes with residual finite scalar quantization, and transfer them to the target city with its static map and the spatial distribution of its residents, thus generating high-quality individual mobilities with adaptation to the target urban environment. In zero-shot cross-city mobility generation, we compare SHAPE with three families of state-of-the-art baselines on four source-to-target cases. SHAPE ranks first on four of the five metrics and at least second on the remaining one. In an oracle test from New York City to Paris, SHAPE uses no individual trajectory of Paris and reaches 90.1% of the OD CPC of an oracle given those trajectories. The extraction of shared activity patterns, rather than reliance on abundant target-city ground truth, is the primary driver of performance improvements. By introducing a city-independent codebook of activity prototypes, our framework effectively balances the trade-off between model capacity and cross-city transfer robustness.