Traffic flow prediction is crucial for public safety and traffic management, and remains a big challenge because of many complicated factors, e.g., multiple spatio-temporal dependencies, holidays, and weather. Some work leveraged 2D convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to explore spatial relations and temporal relations, respectively, which outperformed the classical approaches. However, it is hard for these work to model spatio-temporal relations jointly. To tackle this, some studies utilized LSTMs to connect high-level layers of CNNs, but left the spatio-temporal correlations not fully exploited in low-level layers. In this work, we propose novel spatio-temporal CNNs to extract spatio-temporal features simultaneously from low-level to high-level layers, and propose a novel gated scheme to control the spatio-temporal features that should be propagated through the hierarchy of layers. Based on these, we propose an end-to-end framework, multiple gated spatio-temporal CNNs (MGSTC), for citywide traffic flow prediction. MGSTC can explore multiple spatio-temporal dependencies through multiple gated spatio-temporal CNN branches, and combine the spatio-temporal features with external factors dynamically. Extensive experiments on two real traffic datasets demonstrates that MGSTC outperforms other state-of-the-art baselines.

中文翻译：

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基于多门控时空卷积神经网络的全市交通流量预测

交通流预测对于公共安全和交通管理至关重要，并且由于许多复杂的因素，例如多个时空依赖性、假期和天气，仍然是一个巨大的挑战。一些工作利用 2D 卷积神经网络 (CNN) 和长短期记忆网络 (LSTM) 分别探索空间关系和时间关系，这优于经典方法。然而，这些工作很难共同模拟时空关系。为了解决这个问题，一些研究利用 LSTM 来连接 CNN 的高层，但在低层中没有充分利用时空相关性。在这项工作中，我们提出了新颖的时空 CNN，以同时从低层到高层提取时空特征，并提出了一种新的门控方案来控制应该通过层的层次结构传播的时空特征。基于这些，我们提出了一个端到端框架，即多门控时空 CNN (MGSTC)，用于全市交通流量预测。MGSTC可以通过多个门控时空CNN分支探索多个时空依赖关系，并将时空特征与外部因素动态结合。对两个真实交通数据集的广泛实验表明，MGSTC 优于其他最先进的基线。MGSTC可以通过多个门控时空CNN分支探索多个时空依赖关系，并将时空特征与外部因素动态结合。对两个真实交通数据集的广泛实验表明，MGSTC 优于其他最先进的基线。MGSTC可以通过多个门控时空CNN分支探索多个时空依赖关系，并将时空特征与外部因素动态结合。对两个真实交通数据集的广泛实验表明，MGSTC 优于其他最先进的基线。