Substantial efforts have been devoted to the investigation of spatiotemporal correlations for improving traffic speed prediction accuracy. However, existing works typically model the correlations based solely on the observed traffic state (e.g. traffic speed) without due consideration that different correlation measurements of the traffic data could exhibit a diverse set of patterns under different traffic situations. In addition, the existing works assume that all road segments can employ the same sampling frequency of traffic states, which is impractical. In this paper, we propose new measurements to model the spatial correlations among traffic data and show that the resulting correlation patterns vary significantly under various traffic situations. We propose a Heterogeneous Spatial Correlation (HSC) model to capture the spatial correlation based on a specific measurement, where the traffic data of varying road segments can be heterogeneous (i.e. obtained with different sampling frequency). We propose a Multi-fold Correlation Attention Network (MCAN), which relies on the HSC model to explore multi-fold spatial correlations and leverage LSTM networks to capture multi-fold temporal correlations to provide discriminating features in order to achieve accurate traffic prediction. The learned multi-fold spatiotemporal correlations together with contextual factors are fused with attention mechanism to make the final predictions. Experiments on real-world datasets demonstrate that the proposed MCAN model outperforms the state-of-the-art baselines.

翻译：现已作出大量努力,调查时空关系,以提高交通速度预测的准确性;然而,现有工作通常只是根据观察到的交通状况(如交通速度)来模拟这些相互关系,而没有适当考虑对交通数据的不同相关测量可能在不同交通形势下显示一套不同的模式;此外,现有工作假设所有道路段都可采用交通状态的相同取样频率,这是不切实际的;在本文件中,我们提议进行新的测量,以模拟交通数据之间的空间相关性,并表明在不同交通形势下产生的相关模式差异很大;我们提议采用一种高度的空间关系模型(HSC)模型,以根据具体测量结果来捕捉空间相关性,其中不同道路段的交通数据可能各不相同(即以不同取样频率获得的),因此,不同的交通数据可以显示各种不同的模式;我们提议建立一个多倍的关联关注网络(MCAN),依靠HSC模型来探索多倍的空间相关性,并利用LSTM网络获取多倍的时间关联性,以提供精确的交通预测。我们提议采用一个多倍的模型空间关联性空间关系模型(HSC)模型,以最后的模型为基础,对现实空间空间数据进行模拟的预测,同时展示全球数据结构,以显示与设定的链接的注意。