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.

翻译：交通预测对于智能交通系统（ITS）至关重要，有助于资源高效分配和有效交通管控。然而，其有效性往往高度依赖于充足的交通数据，而许多城市因设备支持有限而缺乏足够数据，这给交通预测带来了重大挑战。针对这一挑战，我们观察到重要现象：不同城市之间的交通模式具有相似性。基于这一关键发现，我们提出了一种针对跨城市少样本交通预测问题的解决方案，称为多尺度交通模式库（MTPB）。首先，MTPB通过利用数据丰富的源城市启动学习过程，通过空间-时间感知预训练过程有效获取全面的交通知识。随后，该框架采用先进的聚类技术，从所学知识中系统性地生成多尺度交通模式库。接着，数据稀缺的目标城市的交通数据可查询该交通模式库，从而促进元知识的聚合。这种元知识在后续的图重构和预测过程中作为稳健的引导起关键作用。基于真实交通数据集的实证评估证实了MTPB的优越性能，其在各类现有方法中表现优异，并展现出众多有利于推进跨城市少样本预测方法发展的特性。代码可在 https://github.com/zhyliu00/MTPB 获取。