  Human trajectory data produced by daily mobile devices has proven its
usefulness in various substantial fields such as urban planning and epidemic
prevention. In terms of the individual privacy concern, human trajectory
simulation has attracted increasing attention from researchers, targeting at
offering numerous realistic mobility data for downstream tasks. Nevertheless,
the prevalent issue of data scarcity undoubtedly degrades the reliability of
existing deep learning models. In this paper, we are motivated to explore the
intriguing problem of mobility transfer across cities, grasping the universal
patterns of human trajectories to augment the powerful Transformer with
external mobility data. There are two crucial challenges arising in the
knowledge transfer across cities: 1) how to transfer the Transformer to adapt
for domain heterogeneity; 2) how to calibrate the Transformer to adapt for
subtly different long-tail frequency distributions of locations. To address
these challenges, we have tailored a Cross-city mObiLity trAnsformer (COLA)
with a dedicated model-agnostic transfer framework by effectively transferring
cross-city knowledge for human trajectory simulation. Firstly, COLA divides the
Transformer into the private modules for city-specific characteristics and the
shared modules for city-universal mobility patterns. Secondly, COLA leverages a
lightweight yet effective post-hoc adjustment strategy for trajectory
simulation, without disturbing the complex bi-level optimization of
model-agnostic knowledge transfer. Extensive experiments of COLA compared to
state-of-the-art single-city baselines and our implemented cross-city baselines
have demonstrated its superiority and effectiveness. The code is available at
https://github.com/Star607/Cross-city-Mobility-Transformer.
