Cross-City Transfer Learning for Deep Spatio-Temporal Prediction
  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 citywide crowd flow
prediction as a demonstration experiment, we verify the effectiveness of
RegionTrans. Results show that RegionTrans can outperform the state-of-the-art
fine-tuning deep spatio-temporal prediction models by reducing up to 10.7%
prediction error.
