  Accurate short-term traffic prediction plays a pivotal role in various smart
mobility operation and management systems. Currently, most of the
state-of-the-art prediction models are based on graph neural networks (GNNs),
and the required training samples are proportional to the size of the traffic
network. In many cities, the available amount of traffic data is substantially
below the minimum requirement due to the data collection expense. It is still
an open question to develop traffic prediction models with a small size of
training data on large-scale networks. We notice that the traffic states of a
node for the near future only depend on the traffic states of its localized
neighborhoods, which can be represented using the graph relational inductive
biases. In view of this, this paper develops a graph network (GN)-based deep
learning model LocaleGN that depicts the traffic dynamics using localized data
aggregating and updating functions, as well as the node-wise recurrent neural
networks. LocaleGN is a light-weighted model designed for training on few
samples without over-fitting, and hence it can solve the problem of few-sample
traffic prediction. The proposed model is examined on predicting both traffic
speed and flow with six datasets, and the experimental results demonstrate that
LocaleGN outperforms existing state-of-the-art baseline models. It is also
demonstrated that the learned knowledge from LocaleGN can be transferred across
cities. The research outcomes can help to develop light-weighted traffic
prediction systems, especially for cities lacking historically archived traffic
data.
