Traffic speed prediction plays a fundamental role in traffic management and driving route planning. However, timely accurate traffic speed prediction is challenging as it is affected by complex spatial and temporal correlations. Most existing works cannot simultaneously model spatial and temporal correlations in traffic data, resulting in unsatisfactory prediction performance. In this article, we propose a novel hybrid deep learning approach, named HDL4TSP, to predict traffic speed in each region of a city, which consists of an input layer, a spatial layer, a temporal layer, a fusion layer, and an output layer. Specifically, first, the spatial layer employs graph convolutional networks to capture spatial near dependencies and spatial distant dependencies in the spatial dimension. Second, the temporal layer employs convolutional long short-term memory (ConvLSTM) networks to model closeness, daily periodicity, and weekly periodicity in the temporal dimension. Third, the fusion layer designs a fusion component to merge the outputs of ConvLSTM networks. Finally, we conduct extensive experiments and experimental results to show that HDL4TSP outperforms four baselines on two real-world data sets.

中文翻译：

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用于交通速度预测的混合深度学习方法


交通速度预测在交通管理和行驶路线规划中起着基础性作用。然而，及时准确的交通速度预测具有挑战性，因为它受到复杂的空间和时间相关性的影响。大多数现有工作无法同时对交通数据中的空间和时间相关性进行建模，导致预测性能不理想。在本文中，我们提出了一种新颖的混合深度学习方法，名为 HDL4TSP，用于预测城市每个区域的交通速度，该方法由输入层、空间层、时间层、融合层和输出层组成。具体来说，首先，空间层采用图卷积网络来捕获空间维度上的空间近依赖关系和空间远依赖关系。其次，时间层采用卷积长短期记忆（ConvLSTM）网络来对时间维度上的紧密度、每日周期性和每周周期性进行建模。第三，融合层设计一个融合组件来合并ConvLSTM网络的输出。最后，我们进行了大量的实验和实验结果，表明 HDL4TSP 在两个真实数据集上优于四个基线。