Zhang Qingyong, Chang Wanfeng, Yin Conghui, Xiao Peng, Li Kelei, Tan Meifang
School of Automation, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China.
Entropy (Basel). 2023 Jun 14;25(6):938. doi: 10.3390/e25060938.
Accurate traffic flow forecasting is very important for urban planning and traffic management. However, this is a huge challenge due to the complex spatial-temporal relationships. Although the existing methods have researched spatial-temporal relationships, they neglect the long periodic aspects of traffic flow data, and thus cannot attain a satisfactory result. In this paper, we propose a novel model Attention-Based Spatial-Temporal Convolution Gated Recurrent Unit (ASTCG) to solve the traffic flow forecasting problem. ASTCG has two core components: the multi-input module and the STA-ConvGru module. Based on the cyclical nature of traffic flow data, the data input to the multi-input module are divided into three parts, near-neighbor data, daily-periodic data, and weekly-periodic data, thus enabling the model to better capture the time dependence. The STA-ConvGru module, formed by CNN, GRU, and attention mechanism, can capture both temporal and spatial dependencies of traffic flow. We evaluate our proposed model using real-world datasets and experiments show that the ASTCG model outperforms the state-of-the-art model.
准确的交通流预测对于城市规划和交通管理非常重要。然而，由于复杂的时空关系，这是一个巨大的挑战。尽管现有方法已经对时空关系进行了研究，但它们忽略了交通流数据的长期周期性特征，因此无法获得令人满意的结果。在本文中，我们提出了一种新颖的基于注意力的时空卷积门控循环单元（ASTCG）模型来解决交通流预测问题。ASTCG有两个核心组件：多输入模块和STA-ConvGru模块。基于交通流数据的周期性，输入到多输入模块的数据被分为三部分，近邻数据、日周期数据和周周期数据，从而使模型能够更好地捕捉时间依赖性。由卷积神经网络（CNN）、门控循环单元（GRU）和注意力机制组成的STA-ConvGru模块可以捕捉交通流的时空依赖性。我们使用真实世界的数据集对我们提出的模型进行评估，实验表明ASTCG模型优于现有最先进的模型。
