The invention discloses a missing traffic flow data interpolation method for a GAN network based on transmission periodicity, and the method proposes a new periodic data extraction mode, extracts the transmission periodicity of various periodic data, is used for the prediction of historical data on missing data, and improves the accuracy of missing data interpolation. A dynamic missing rate reconstruction loss function is designed, the function dynamically adapts to data with different missing rates in different time periods, the randomness and the dynamic nature of a real missing data set are better coped with, the optimization process of the loss function is smoothed, and the robustness and the generalization ability of an interpolation model are improved; according to the method, a dynamic adjacency matrix is constructed, a dynamic spatial relationship between nodes changing along with time is modeled, static spatial dependency and dynamic spatial dependency are considered, and the feature extraction capability of the model for the spatial-temporal correlation of traffic flow data is improved. Compared with the STGAN model, the data interpolation method has the advantage that the interpolation effect is better under the condition of different missing rates on the PeMS04 data set.
本发明公开一种基于传递周期性的GAN网络的缺失交通流数据插补方法,该方法提出了一种新的周期性数据提取方式,提取多种周期数据的传递周期性,用于历史数据对缺失数据的预测,进而提高对缺失数据插补的准确性;设计一种动态缺失率重构损失函数,通过此函数动态的适应不同时段下不同缺失率的数据,更好地应对真实缺失数据集的随机性和动态性,平滑损失函数优化过程,提高插补模型的鲁棒性和泛化能力;构建动态邻接矩阵,对随时间变化的节点之间的动态空间关系进行建模,并考虑了静态空间依赖性和动态空间依赖性,提升模型对交通流数据时空相关性的特征提取能力。该数据插补方法比STGAN模型在PeMS04数据集上的不同缺失率情况下的插补效果均更好。
Missing traffic flow data interpolation method of GAN network based on transmission periodicity
一种基于传递周期性的GAN网络的缺失交通流数据插补方法
2024-11-22
Patent
Electronic Resource
Chinese
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