Accurate and timely predicting citywide traffic crowd flows precisely is crucial for public safety and traffic management in smart cities. Nevertheless, its crucial challenge lies in how to model multiple complicated spatial dependencies between different regions, dynamic temporal laws among different time intervals with external factors such as holidays, events, and weather. Some existing work leverage the long short-term memory (LSTM) and convolutional neural network (CNN) to explore temporal relations and spatial relations, respectively; which have outperformed the classical statistical methods. However, it is difficult for these approaches to jointly model spatial and temporal correlations. To address this problem, we propose a dynamic deep hybrid spatio-temporal neural network namely DHSTNet, to predict traffic flows in every region of a city with high accuracy. In particular, our DSHTNet model comprises four properties i.e., closeness volume, daily volume, trend volume, and external branch, respectively. Moreover, the projected model dynamically assigns different weights to various branches and, then, integrate outputs of four properties to produce final prediction outcomes. The model has been evaluated, both for offline and online predictions, using an edge/fog infrastructure where training happens on the remote cloud and prediction occurs at the edge i.e. in the proximity of users. Extensive experiments and evaluation on two real-world datasets demonstrate the advantage of the proposed model, in terms of high accuracy over prevailing state-of-the-art baseline methods. Moreover, we apply the exaggeration approach based on an attention mechanism to the above model, called as AAtt-DHSTNet; to predict citywide short-term traffic crowd flows; and show its notable performance in the traffic flows prediction. The aggregation method collects information from the related time series, remove redundancy and, thus, increases prediction speed and accuracy. Our empirical evaluation suggests that the AAtt-DHSTNet model is approximately 20.8% and 8.8% more accurate than the DHSTNet technique, for two different real-world traffic datasets.

准确，及时地预测整个城市的交通流量对智能城市的公共安全和交通管理至关重要。然而，它的关键挑战在于如何对不同区域之间的多个复杂空间相关性，不同时间间隔之间的动态时间规律以及外部因素（如假期，事件和天气）进行建模。现有的一些工作利用长短期记忆（LSTM）和卷积神经网络（CNN）分别探索时间关系和空间关系。其性能优于传统的统计方法。但是，这些方法很难共同对空间和时间相关性进行建模。为了解决这个问题，我们提出了动态深度混合时空神经网络DHSTNet，以高精度预测城市每个区域的交通流量。特别是，我们的DSHTNet模型包含四个属性，分别是收盘量，每日量，趋势量和外部分支。此外，投影模型动态地将不同的权重分配给各个分支，然后将四个属性的输出进行积分以产生最终的预测结果。已使用边缘/雾基础结构对模型进行了脱机和在线预测的评估，其中，培训在远程云上进行，而预测在边缘（即用户附近）进行。在两个真实世界的数据集上进行的大量实验和评估证明了该模型的优势，即相对于当前最先进的基线方法而言，其准确性更高。此外，我们将基于注意力机制的夸张方法应用于上述模型AAtt-DHSTNet。预测全市范围内的短期交通流量；并在交通流量预测中显示出其显着的性能。聚合方法从相关的时间序列中收集信息，消除冗余，从而提高预测速度和准确性。我们的经验评估表明，对于两个不同的实际流量数据集，AAtt-DHSTNet模型的精度比DHSTNet技术高出约20.8％和8.8％。提高预测速度和准确性。我们的经验评估表明，对于两个不同的实际流量数据集，AAtt-DHSTNet模型的精度比DHSTNet技术高出约20.8％和8.8％。提高预测速度和准确性。我们的经验评估表明，对于两个不同的实际流量数据集，AAtt-DHSTNet模型的精度比DHSTNet技术高出约20.8％和8.8％。