Traffic flow prediction plays an important role in ITS (Intelligent Transportation System). This task is challenging due to the complex spatial and temporal correlations (e.g., the constraints of road network and the law of dynamic change with time). Existing work tried to solve this problem by exploiting a variety of spatiotemporal models. However, we observe that more semantic pair-wise correlations among possibly distant roads are also critical for traffic flow prediction. To jointly model the spatial, temporal, semantic correlations with various global features in the road network, this paper proposes T-MGCN (Temporal Multi-Graph Convolutional Network), a deep learning framework for traffic flow prediction. First, we identify several kinds of semantic correlations, and encode the non-Euclidean spatial correlations and heterogeneous semantic correlations among roads into multiple graphs. These correlations are then modeled by a multi-graph convolutional network. Second, a recurrent neural network is utilized to learn dynamic patterns of traffic flow to capture the temporal correlations. Third, a fully connected neural network is utilized to fuse the spatiotemporal correlations with global features. We evaluate T-MGCN on two real-world traffic datasets and observe improvement by approximately 3% to 6% as compared to the state-of-the-art baseline.

交通流预测作为智能交通系统的一个关键问题,是国内外交通领域的研究热点.交通流预测的主要挑战在于交通流数据本身具有复杂的时空关联,且易受各种社会事件的影响.针对这些挑战,提出一种用于交通流预测的深度学习框架.一方面,针对道路网络非欧氏的空间关联以及交通流时序数据的时间关联,设计了一种融合图卷积神经网络和循环神经网络的特征抽取子网络;另一方面,针对社会事件对交通流的潜在影响,设计了一种基于卷积神经网络的社会事件特征抽取子网络.最后,融合时空关联特征抽取子网络和社会事件特征抽取子网络,实现交通流预测模型.为了验证模型的有效性,文中基于真实交通流数据进行了实验.结果表明,所提模型与传统的预测模型相比具有较高的准确度,准确度提高了3％～6％.