  Traffic forecasting influences various intelligent transportation system
(ITS) services and is of great significance for user experience as well as
urban traffic control. It is challenging due to the fact that the road network
contains complex and time-varying spatial-temporal dependencies. Recently, deep
learning based methods have achieved promising results by adopting graph
convolutional network (GCN) to extract the spatial correlations and recurrent
neural network (RNN) to capture the temporal dependencies. However, the
existing methods often construct the graph only based on road network
connectivity, which limits the interaction between roads. In this work, we
propose Geographic and Long term Temporal Graph Convolutional Recurrent Neural
Network (GLT-GCRNN), a novel framework for traffic forecasting that learns the
rich interactions between roads sharing similar geographic or longterm temporal
patterns. Extensive experiments on a real-world traffic state dataset validate
the effectiveness of our method by showing that GLT-GCRNN outperforms the
state-of-the-art methods in terms of different metrics.
