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.
翻译:准确的实时交通预报对于智能交通系统(ITS)至关重要,并且是各种智能交通应用的基石。虽然这一研究领域以深层学习为主,但最近的研究表明,通过开发新的模型结构,准确性改进正在变得微不足道。相反,我们设想通过在具有不同数据分布和网络地形的城市中转让“预测相关知识”可以实现改进。为此,本文件旨在提出一个新的可转移交通预报框架:Domain Adversarial空间-时空网络(DASTNet) 。DASTNet在多个来源网络上进行了预先培训,并且与目标网络的交通数据进行微调。具体地说,我们利用图表代表学习和对立域域适应技术来学习域变换嵌嵌嵌,这些技术被进一步纳入时间流量数据的模型。根据我们的知识,我们首先对全网络交通预测问题采用对抗性多面适应性适应。DASTNet在三个基准数据集上始终超越了所有最先进的基线方法。我们利用了这个经过培训的DASTNet来进行最新的流量预测,在三个基准数据集上可以立即对香港进行最新的预测。