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 this https URL

交通预测对智能交通系统至关重要，而许多城市由于设备支持有限而缺乏足够的数据，这给交通预测带来了重大挑战。我们提出了一个解决跨城少样本交通预测问题的解决方案，名为多尺度交通模式库 (MTPB)。通过利用数据丰富的源城市，MTPB 通过空时感知的预训练过程有效地获取综合的交通知识，并使用先进的聚类技术系统地生成来自学到的知识的多尺度交通模式库。实证评估结果表明，MTPB 在真实世界交通数据集上具有出色的性能，在各个类别上超越了现有方法，并展现了对跨城少样本预测方法推进的诸多优点。

跨城市少样本交通预测的多尺度交通模式库

Multi-scale Traffic Pattern Bank for Cross-city Few-shot Traffic  Forecasting

Data insufficiency problems (i.e., data missing and label scarcity) caused by
inadequate services and infrastructures or imbalanced development levels of
cities have seriously affected the urban computing tasks in real scenarios.
Prior transfer learning methods inspire an elegant solution to the data
insufficiency, but are only concerned with one kind of insufficiency issue and
fail to give consideration to both sides. In addition, most previous cross-city
transfer methods overlook inter-city data privacy which is a public concern in
practical applications. To address the above challenging problems, we propose a
novel Cross-city Federated Transfer Learning framework (CcFTL) to cope with the
data insufficiency and privacy problems. Concretely, CcFTL transfers the
relational knowledge from multiple rich-data source cities to the target city.
Besides, the model parameters specific to the target task are firstly trained
on the source data and then fine-tuned to the target city by parameter
transfer. With our adaptation of federated training and homomorphic encryption
settings, CcFTL can effectively deal with the data privacy problem among
cities. We take the urban region profiling as an application of smart cities
and evaluate the proposed method with a real-world study. The experiments
demonstrate the notable superiority of our framework over several competitive
state-of-the-art methods.

提出一种新的跨城联邦迁移学习框架 (CcFTL) 来解决数据不足和隐私问题，将多个数据丰富的城市的关系知识传递到目标城市，并针对目标任务在源数据上训练模型参数，然后进行参数传递，通过联邦训练和同态加密设置，CcFTL 能够有效地处理城市之间的数据隐私问题，并将该方法应用于智慧城市的城市区域配置，实验结果表明，CcFTL 方法优于多种最先进的竞争方法。