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SSGRU: A novel hybrid stacked GRU-based traffic volume prediction approach in a road network

机译：SSGRU：道路网络中的一种新型混合基于GRU的交通量预测方法

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摘要

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.
机译：作为缓解交通拥堵和帮助构建更安全的交通系统的潜在解决方案，近年来交通流预测方法很大。在以前的研究中，可以找到机器学习（ML）的基础方法广泛应用于单道路的体积预测。然而，当应用于更复杂的道路网络时，它们通常会效率低，需要支付更高的计算成本。为了解决这个问题，本文提出了一种名为所选堆叠门控复发单元模型（SSGRU）的创新的ML的模型，主要是暗示道路网络流量。该模型中主要有两个部分：一个用于基于线性回归系数进行空间模式挖掘，另一个包括堆叠门控复发单元（SGRU），这对于多道路业务流预测至关重要。作为基本单元，采用简单的树结构来近似于给定的道路网络。特别是，我们将模型实施到郊区和城市交通环境中，以证明其高适应性。整个评估过程基于以15分钟的间隔记录的七种不同的交通量数据集，从英格兰高速公路中选择。结果表明，当应用于所有道路情景的多道路输入基础设施时，我们的模型比其他更高的准确性。

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