  Traffic forecasting is crucial for intelligent transportation systems (ITS),
aiding in efficient resource allocation and effective traffic control. However,
its effectiveness often relies heavily on abundant traffic data, while many
cities lack sufficient data due to limited device support, posing a significant
challenge for traffic forecasting. Recognizing this challenge, we have made a
noteworthy observation: traffic patterns exhibit similarities across diverse
cities. Building on this key insight, we propose a solution for the cross-city
few-shot traffic forecasting problem called Multi-scale Traffic Pattern Bank
(MTPB). Primarily, MTPB initiates its learning process by leveraging data-rich
source cities, effectively acquiring comprehensive traffic knowledge through a
spatial-temporal-aware pre-training process. Subsequently, the framework
employs advanced clustering techniques to systematically generate a multi-scale
traffic pattern bank derived from the learned knowledge. Next, the traffic data
of the data-scarce target city could query the traffic pattern bank,
facilitating the aggregation of meta-knowledge. This meta-knowledge, in turn,
assumes a pivotal role as a robust guide in subsequent processes involving
graph reconstruction and forecasting. Empirical assessments conducted on
real-world traffic datasets affirm the superior performance of MTPB, surpassing
existing methods across various categories and exhibiting numerous attributes
conducive to the advancement of cross-city few-shot forecasting methodologies.
The code is available in https://github.com/zhyliu00/MTPB.
