针对城市路网短时交通流预测问题，在考虑路网交通状态时空相关性基础上，提出了一种基于图Transformer（graph transformer, Graformer）的预测方法。该方法将多条路段的交通状态预测问题转化为图节点状态预测问题，针对区分相同结构的空间路网结构图，将带有边的图同构网络（graph isomorphism network with edges, GINE）和Transformer网络相结合，对交通状态在路网层面的时空相关性进行建模，从而实现城市路网短时交通流预测。具体来说，Graformer模型首先利用长短期记忆网络（long short-term memory, LSTM）对交通数据的时序信息进行预处理，接着采用基于GINE与Transformer的全局注意力机制提取交通数据的空间特征，最后实现路网各路段交通流的同步预测。

A prediction method based on graph transformer (Graformer) was proposed for short-term traffic flow prediction in urban road networks, considering the spatiotemporal correlation of road network traffic status. This method transformed the traffic state prediction problem of multiple road sections into a graph node state prediction problem. For distinguishing spatial road network structures with the same structure, a graph isomorphism network with edges (GINE) and a Transformer network to model the spatiotemporal correlation of traffic states at the road network level were combined, thereby achieving short-term traffic flow prediction for urban road networks. Specifically, the Graformer model first used the long short term memory (LSTM) network to preprocess the temporal information of the traffic data, then used the global attention mechanism based on GINE and Transformer to extract the spatial characteristics of the traffic data, and finally realized the synchronous prediction of the traffic flow of each road section of the road network. Through experimental verification using the PeMS dataset, the results show that the proposed Graformer model outperformed the comparative model in various performance indicators, demonstrating its effectiveness as a reliable and efficient short-term traffic flow prediction method for road networks.