The problem of traffic congestion not only causes a large amount of economic
losses, but also seriously endangers the urban environment. Predicting traffic
congestion has important practical significance. So far, most studies have been
based on historical data from sensors placed on different roads to predict
future traffic flow and speed, to analyze the traffic congestion conditions of
a certain road segment. However, due to the fixed position of sensors, it is
difficult to mine new information. On the other hand, vehicle trajectory data
is more flexible and can extract traffic information as needed. Therefore, we
proposed a new traffic congestion prediction model - Multi Adjacency
relationship Attention Graph Convolutional Networks(MA2GCN). This model
transformed vehicle trajectory data into graph structured data in grid form,
and proposed a vehicle entry and exit matrix based on the mobility between
different grids. At the same time, in order to improve the performance of the
model, this paper also built a new adaptive adjacency matrix generation method
and adjacency matrix attention module. This model mainly used gated temporal
convolution and graph convolution to extract temporal and spatial information,
respectively. Compared with multiple baselines, our model achieved the best
performance on Shanghai taxi GPS trajectory dataset. The code is available at
this https URL Traffic Benchmark.

提出了一种新的交通拥堵预测模型 - 多邻接关系注意力图卷积网络（MA2GCN），通过将车辆轨迹数据转换为以网格形式的图结构化数据，提出了基于不同网格之间的移动性的车辆进出矩阵。同时，为了提高模型性能，还构建了一种新的自适应邻接矩阵生成方法和邻接矩阵注意力模块。该模型主要利用时序卷积和图卷积分别提取时空信息，并在上海出租车 GPS 轨迹数据集上表现出最佳性能。