  With the rapid growth of traffic sensors deployed, a massive amount of
traffic flow data are collected, revealing the long-term evolution of traffic
flows and the gradual expansion of traffic networks. How to accurately
forecasting these traffic flow attracts the attention of researchers as it is
of great significance for improving the efficiency of transportation systems.
However, existing methods mainly focus on the spatial-temporal correlation of
static networks, leaving the problem of efficiently learning models on networks
with expansion and evolving patterns less studied. To tackle this problem, we
propose a Streaming Traffic Flow Forecasting Framework, TrafficStream, based on
Graph Neural Networks (GNNs) and Continual Learning (CL), achieving accurate
predictions and high efficiency. Firstly, we design a traffic pattern fusion
method, cleverly integrating the new patterns that emerged during the long-term
period into the model. A JS-divergence-based algorithm is proposed to mine new
traffic patterns. Secondly, we introduce CL to consolidate the knowledge
learned previously and transfer them to the current model. Specifically, we
adopt two strategies: historical data replay and parameter smoothing. We
construct a streaming traffic dataset to verify the efficiency and
effectiveness of our model. Extensive experiments demonstrate its excellent
potential to extract traffic patterns with high efficiency on long-term
streaming network scene. The source code is available at
https://github.com/AprLie/TrafficStream.
