考虑路网拓扑时变的交通拥堵自适应预测方法研究

对路网交通系统中的交通拥堵进行预测，有利于交通管理和避免交通风险。然而，由于交通管制、道路施工、恶劣天气、自然灾害等原因，路网交通系统的拓扑结构时常发生变化，使得依赖于固定路网拓扑的拥堵预测方法效果不佳。针对这一问题，提出一种双重自适应图卷积循环网络结构(DAGCRN)来处理路网拓扑结构变化情况下的交通拥堵预测问题，该方法运用自适应辅助邻接矩阵对预定义的路网静态图结构进行适应性学习以动态优化原有连接间信息的传递，运用自适应嵌入邻接矩阵对预定义路网静态图结构进行路网隐藏信息的捕捉以确保路网拓扑结构的动态完整性，并采用门控循环单元提取路网交通流的时间特征信息。研究结果表明,DAGCRN具备以下特点：①能够有效捕捉和定位路网拓扑结构发生的变化，并能够在拓扑结构变化时仍然保证拥堵预测的精确率；②相比较一些常见预测模型有更高的预测准确率，尤其是长期预测方面和克服路网结构变化方面更具优势；③进一步的双重自适应功能消融试验，证实了含有自适应辅助邻接矩阵和自适应嵌入邻接矩阵的双重自适应图卷积结构对于路网拓扑结构变化有很强的自适应能力，缺少2个或任一个自适应模块，都会引起模型预测性能的大幅下降。

Research on an Adaptive Traffic Congestion Prediction Method Considering a Time-varying Network Topology

Predicting traffic congestion in traffic systems is beneficial for traffic management and reduces traffic risks. However, owing to traffic control, road construction, bad weather, natural disasters, and other reasons, the topology of road network traffic systems often changes; therefore, congestion prediction methods relying on a fixed road network topology are not effective. To solve this problem, this paper proposes a dual-adaptive graph convolution recurrent network architecture (DAGCRN) to predict traffic congestion in a dynamic time-varying road network topology. In this network, an adaptive auxiliary adjacency matrix is used to learn the static graph structure of a pre-defined road network, and the transferred information among the original connections is optimized dynamically, thereby overcoming the information uncertainty caused by changes in the road network topology. An adaptive embedding adjacency matrix is used to capture the hidden information of a predefined road network. This ensures the dynamic integrity of the road network topology. A gated recurrent unit is used to extract the temporal characteristic information of the traffic flow in road networks. Experimental results demonstrate that ① the proposed DAGCRN can effectively capture and locate changes in the topology structure of a road network and still ensure the accuracy of congestion prediction when the topology changes; ② compared with some common prediction models, the predictive accuracy is higher, especially in terms of long-term prediction, while overcoming changes in the structure of the road network; and ③ further results from a dual-adaptive function ablation experiment confirm that the dual-adaptive graph convolution structure with adaptive auxiliary adjacency matrix and adaptive embedded adjacency matrix has a strong adaptive ability to time-varying road networks, and the absence of any adaptive module would lead to a significant decline in model prediction performance.