The field of Intelligent Transportation Systems (ITS) relies on accurate
traffic forecasting to enable various downstream applications. However,
developing cities often face challenges in collecting sufficient training
traffic data due to limited resources and outdated infrastructure. Recognizing
this obstacle, the concept of cross-city few-shot forecasting has emerged as a
viable approach. While previous cross-city few-shot forecasting methods ignore
the frequency similarity between cities, we have made an observation that the
traffic data is more similar in the frequency domain between cities. Based on
this fact, we propose a \textbf{F}requency \textbf{E}nhanced
\textbf{P}re-training Framework for \textbf{Cross}-city Few-shot Forecasting
(\textbf{FEPCross}). FEPCross has a pre-training stage and a fine-tuning stage.
In the pre-training stage, we propose a novel Cross-Domain Spatial-Temporal
Encoder that incorporates the information of the time and frequency domain and
trains it with self-supervised tasks encompassing reconstruction and
contrastive objectives. In the fine-tuning stage, we design modules to enrich
training samples and maintain a momentum-updated graph structure, thereby
mitigating the risk of overfitting to the few-shot training data. Empirical
evaluations performed on real-world traffic datasets validate the exceptional
efficacy of FEPCross, outperforming existing approaches of diverse categories
and demonstrating characteristics that foster the progress of cross-city
few-shot forecasting.

我们提出了一种基于频率增强的预训练框架 FEPCross，用于跨城市少样本预测，通过引入时间和频率领域的信息进行自监督训练，以及设计模块来丰富训练样本并维护动量更新的图结构，从而降低过拟合风险和提高预测效果，实验证实了 FEPCross 在真实交通数据集上的卓越性能和促进跨城市少样本预测进展的特性。