As a potential solution to relieve traffic congestions and help build a more safe traffic system, traffic flow prediction methods are given much attention in recent years. In previous studies, it can be found the machine learning (ML)-based methods are widely used in volume predictions of single roads. However, when applied in a more complicated road network, they usually show low efficiency and need to pay higher computing costs. To solve this problem, an innovative ML-based model, named Selected Stacked Gated Recurrent Units model (SSGRU), is proposed in this paper, which is mainly in allusion to road network traffic flow. There are mainly two parts in this model: one is used to do spatial pattern mining based on linear regression coefficients, and the other one includes a stacked gated recurrent unit (SGRU), which is essential for multi-road traffic flow prediction. As the basic unit, a simple tree structure is adopted to approximate the given road network. Particularly, we implemented our model into both suburban and urban traffic contexts, to prove its high adaptability. The whole evaluation process is based on seven different traffic volume data sets recorded at the 15-min interval, chosen from the England Highways. The results show that our model has higher accuracy than others when applied to a multi-road input infrastructure for all road scenarios.

作为缓解交通拥堵并帮助建立更安全的交通系统的潜在解决方案，交通流量预测方法近年来受到了广泛关注。在以前的研究中，可以发现基于机器学习（ML）的方法已广泛用于单条道路的交通量预测。但是，当应用于更复杂的道路网络时，它们通常显示出较低的效率，并且需要支付更高的计算成本。为了解决这个问题，本文提出了一种创新的基于ML的模型，即选定的堆叠门控循环单元模型（SSGRU），主要是针对道路网络的交通流量。此模型主要由两部分组成：一个部分用于基于线性回归系数进行空间模式挖掘，另一部分包括堆叠式门控递归单元（SGRU），这对于多路交通流量预测至关重要。作为基本单位，采用简单的树结构来近似给定的道路网络。特别是，我们将模型应用于郊区和城市交通环境，以证明其高度的适应性。整个评估过程基于选自英格兰高速公路的以15分钟为间隔记录的七个不同的交通量数据集。结果表明，当将模型应用于所有道路场景的多道路输入基础结构时，该模型具有比其他模型更高的准确性。整个评估过程基于选自英格兰高速公路的以15分钟为间隔记录的七个不同的交通量数据集。结果表明，当将模型应用于所有道路场景的多道路输入基础结构时，该模型具有比其他模型更高的准确性。整个评估过程基于选自英格兰高速公路的以15分钟为间隔记录的七个不同的交通量数据集。结果表明，当将模型应用于所有道路场景的多道路输入基础结构时，该模型具有比其他模型更高的准确性。