基于时空位置关注图神经网络的交通流预测方法  

Traffic flow prediction method based on spatial temporal positionattention graph neural network

作　　者：何婷 周艳秋 辛春花 He Ting;Zhou Yanqiu;Xin Chunhua(Dept.of Computer Technology&Information Management,Inner Mongolia Agricultural University,Baotou Nei Mongol 010010,China)

机构地区：[1]内蒙古农业大学计算机技术与信息管理系,内蒙古包头010010

出　　处：《计算机应用研究》2024年第10期2932-2938,共7页Application Research of Computers

基　　金：国家自然科学基金资助项目(31960361);内蒙古自治区科技计划资助项目(2020GG0033)。

摘　　要：针对现有交通流预测方法存在难以构建空间和时间依赖关系的问题,提出了新的利用时空位置注意力的图神经网络(ST-PAGNN)方法。首先,该图神经网络中包含有位置关注机制,由此能够更好地对城市道路网络中交通节点的空间依赖关系进行有效捕捉;然后,利用带有trend adaptive Transformer(Trendformer)的门控递归神经网络来捕捉交通流序列在时间维度上的局部和全局信息;最后,利用改进的网格搜索优化方法对模型的引入参数进行优化,并以较高的时间效率获得全局最优解。实验结果表明,在数据集PEMS-BAY中,预测步长分别为15 min,30 min,60 min时,ST-PAGNN的评价指标RMSE、MAE和MAPE分别为1.37,2.57,2.67%,1.55,3.64,3.37%,1.97,4.37,4.43%;在数据集METR-LA中,预测步长分别为15 min,30 min,60 min时,ST-PAGNN的评价指标RMSE、MAE和MAPE分别为2.73,5.16,7.13%,2.99,5.97,7.86%,3.53,7.16,9.96%。结论表明,ST-PAGNN在不同粒度下的评价指标中均高于现有模型,从而说明了ST-PAGNN在解决交通预测问题方面的有效性和优越性。To address the challenge of constructing spatial and temporal dependencies in existing traffic flow prediction me-thods,this paper proposed a new method called spatial temporal position attention graph neural network(ST-PAGNN),which utilized spatiotemporal location attention.Firstly,the graph neural network contained a location attention mechanism,which could better capture the spatial dependence of traffic nodes in the urban road network.Then,it used a gated recurrent neural network with trend adaptive transformer(Trendformer)to capture the local and global information of the traffic flow sequence in the time dimension.Finally,it used the improved grid search optimization method to optimize the introduced para-meters of the model,obtaining the global optimal solution with high time efficiency.The experimental results show that in the dataset PEMS-BAY,the evaluation indexes RMSE,MAE and MAPE of the ST-PAGNN method are 1.37,2.57,2.67%,1.55,3.64,3.37%,1.97,4.37 and 4.43%,respectively,when the prediction step size is 15 min,30 min and 60 min,respectively.In the dataset METR-LA,when the prediction step size is 15 min,30 min and 60 min,the evaluation indexes RMSE,MAE and MAPE of the ST-PAGNN method are 2.73,5.16,7.13%,2.99,5.97,7.86%,3.53,7.16 and 9.96%,respectively.The results show that the proposed ST-PAGNN method is higher than the existing models in the evaluation indexes under different granularities,which illustrates the effectiveness and superiority of ST-PAGNN in solving traffic prediction problems.

关 键 词：ST-PAGNN 交通流预测 深度学习 图卷积神经网络 门控循环单元 Trendformer 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]