Abstract
Spatio-temporal prediction is a key type of tasks in
urban computing, e.g., traffic flow and air quality.
Adequate data is usually a prerequisite, especially
when deep learning is adopted. However, the development levels of different cities are unbalanced,
and still many cities suffer from data scarcity. To
address the problem, we propose a novel cross-city
transfer learning method for deep spatio-temporal
prediction tasks, called RegionTrans. RegionTrans aims to effectively transfer knowledge from
a data-rich source city to a data-scarce target city.
More specifically, we first learn an inter-city region
matching function to match each target city region
to a similar source city region. A neural network
is designed to effectively extract region-level representation for spatio-temporal prediction. Finally,
an optimization algorithm is proposed to transfer
learned features from the source city to the target city with the region matching function. Using
crowd flow prediction as a demonstration experiment, we verify the effectiveness of RegionTrans