  As the development of cities, traffic congestion becomes an increasingly
pressing issue, and traffic prediction is a classic method to relieve that
issue. Traffic prediction is one specific application of spatio-temporal
prediction learning, like taxi scheduling, weather prediction, and ship
trajectory prediction. Against these problems, classical spatio-temporal
prediction learning methods including deep learning, require large amounts of
training data. In reality, some newly developed cities with insufficient
sensors would not hold that assumption, and the data scarcity makes predictive
performance worse. In such situation, the learning method on insufficient data
is known as few-shot learning (FSL), and the FSL of traffic prediction remains
challenges. On the one hand, graph structures' irregularity and dynamic nature
of graphs cannot hold the performance of spatio-temporal learning method. On
the other hand, conventional domain adaptation methods cannot work well on
insufficient training data, when transferring knowledge from different domains
to the intended target domain.To address these challenges, we propose a novel
spatio-temporal domain adaptation (STDA) method that learns transferable
spatio-temporal meta-knowledge from data-sufficient cities in an adversarial
manner. This learned meta-knowledge can improve the prediction performance of
data-scarce cities. Specifically, we train the STDA model using a
Model-Agnostic Meta-Learning (MAML) based episode learning process, which is a
model-agnostic meta-learning framework that enables the model to solve new
learning tasks using only a small number of training samples. We conduct
numerous experiments on four traffic prediction datasets, and our results show
that the prediction performance of our model has improved by 7\% compared to
baseline models on the two metrics of MAE and RMSE.
