Accurate real-time traffic forecast is critical for intelligent
transportation systems (ITS) and it serves as the cornerstone of various smart
mobility applications. Though this research area is dominated by deep learning,
recent studies indicate that the accuracy improvement by developing new model
structures is becoming marginal. Instead, we envision that the improvement can
be achieved by transferring the "forecasting-related knowledge" across cities
with different data distributions and network topologies. To this end, this
paper aims to propose a novel transferable traffic forecasting framework:
Domain Adversarial Spatial-Temporal Network (DASTNet). DASTNet is pre-trained
on multiple source networks and fine-tuned with the target network's traffic
data. Specifically, we leverage the graph representation learning and
adversarial domain adaptation techniques to learn the domain-invariant node
embeddings, which are further incorporated to model the temporal traffic data.
To the best of our knowledge, we are the first to employ adversarial
multi-domain adaptation for network-wide traffic forecasting problems. DASTNet
consistently outperforms all state-of-the-art baseline methods on three
benchmark datasets. The trained DASTNet is applied to Hong Kong's new traffic
detectors, and accurate traffic predictions can be delivered immediately
(within one day) when the detector is available. Overall, this study suggests
an alternative to enhance the traffic forecasting methods and provides
practical implications for cities lacking historical traffic data.

该论文提出了一种新的可迁移交通预测框架 —— 域对抗时空网络（DASTNet），该框架通过预先训练多个源网络并使用目标网络的流量数据进行微调来实现跨不同城市的交通预测。使用图表示学习和对抗性领域适应技术来学习领域不变节点嵌入，并将其进一步融入到模型中来进行时空交通数据建模。在三个基准数据集上的实验表明，DASTNet 始终优于所有最先进的基线方法。

域对抗时空网络：一种适用于跨城市短期交通预测的可迁移框架

Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities

Timely accurate traffic forecast is crucial for urban traffic control and
guidance. Due to the high nonlinearity and complexity of traffic flow,
traditional methods cannot satisfy the requirements of mid-and-long term
prediction tasks and often neglect spatial and temporal dependencies. In this
paper, we propose a novel deep learning framework, Spatio-Temporal Graph
Convolutional Networks (STGCN), to tackle the time series prediction problem in
traffic domain. Instead of applying regular convolutional and recurrent units,
we formulate the problem on graphs and build the model with complete
convolutional structures, which enable much faster training speed with fewer
parameters. Experiments show that our model STGCN effectively captures
comprehensive spatio-temporal correlations through modeling multi-scale traffic
networks and consistently outperforms state-of-the-art baselines on various
real-world traffic datasets.

本文提出一个基于图卷积神经网络（STGCN）的深度学习框架来预测交通领域的时间序列问题，该模型可以更快地训练少量参数，通过建模多尺度交通网络，有效地捕捉了全面的时空相关性，并在各种真实世界的交通数据集上不断超越现有最先进的基线模型。