Li Cong, Zhang Huyin, Wang Zengkai, Wu Yonghao, Yang Fei
School of Computer Science, Wuhan University, Wuhan, China.
Department of Information Engineering, Wuhan Institute of City, Wuhan, China.
Front Neurorobot. 2022 Jul 6;16:925210. doi: 10.3389/fnbot.2022.925210. eCollection 2022.
Urban transportation destination prediction is a crucial issue in the area of intelligent transportation, such as urban traffic planning and traffic congestion control. The spatial structure of the road network has high nonlinearity and complexity, and also, the traffic flow is dynamic due to the continuous changing of the traffic environment. Thus, it is very important to model the spatial relation and temporal dependence simultaneously to simulate the true traffic conditions. Most of the existing destination prediction methods have limited ability to model large-scale spatial data that changes dynamically with time, so they cannot obtain satisfactory prediction results. This paper proposes a human-in-loop Spatial-Temporal Attention Mechanism with Graph Convolutional Network (STAGCN) model to explore the spatial-temporal dependencies for destination prediction. The main contributions of this study are as follows. First, the traffic network is represented as a graph network by grid region dividing, then the spatial-temporal correlations of the traffic network can be learned by convolution operations in time on the graph network. Second, the attention mechanism is exploited for the analysis of features with loop periodicity and enhancing the features of key nodes in the grid. Finally, the spatial and temporal features are combined as the input of the Long-Short Term Memory network (LSTM) to further capture the spatial-temporal dependences of the traffic data to reach more accurate results. Extensive experiments conducted on the large scale urban real dataset show that the proposed STAGCN model has achieved better performance in urban car-hailing destination prediction compared with the traditional baseline models.
城市交通目的地预测是智能交通领域的一个关键问题，例如城市交通规划和交通拥堵控制。道路网络的空间结构具有高度的非线性和复杂性，而且由于交通环境的不断变化，交通流是动态的。因此，同时对空间关系和时间依赖性进行建模以模拟真实交通状况非常重要。现有的大多数目的地预测方法对随时间动态变化的大规模空间数据进行建模的能力有限，因此无法获得令人满意的预测结果。本文提出了一种基于图卷积网络的人在回路时空注意力机制（STAGCN）模型，以探索用于目的地预测的时空依赖性。本研究的主要贡献如下。首先，通过网格区域划分将交通网络表示为图网络，然后通过在图网络上进行时间上的卷积运算来学习交通网络的时空相关性。其次，利用注意力机制分析具有循环周期性的特征，并增强网格中关键节点的特征。最后，将空间和时间特征作为长短期记忆网络（LSTM）的输入，以进一步捕捉交通数据的时空依赖性，从而获得更准确的结果。在大规模城市真实数据集上进行的大量实验表明，与传统的基线模型相比，所提出的STAGCN模型在城市网约车目的地预测方面取得了更好的性能。
