针对现有交通流预测模型在长时预测中精度有限以及训练代价高的问题，构建一种基于跨周期稀疏技术的长时交通流预测模型。考虑到交通流数据复杂的周期性和长时预测时间跨度增加带来的误差累积和模型复杂度增加，采用跨周期稀疏技术对时间序列下采样处理，对长时序列进行周期分解再拼接转置形成时间序列矩阵。将混合图卷积网络嵌入门控循环单元来处理下采样后的时间序列矩阵，最后使用带有时间依赖矩阵的时间多头注意力模块来融合相关时空特征。对3个公开交通流数据集PeMS-bay、PeMS04和PeMS08处理后进行有效性验证。结果表明：所建模型在3个数据集上的预测精度指标均优于基线模型，平均预测精度在3个数据集上分别提升了5.59%、8.56%和7.11%且模型参数量是基线模型的14.81% ~ 27.17%。

In response to the limited accuracy and high training costs of existing traffic flow forecasting models in long-term forecasting, a long-term traffic flow forecasting model based on cross period sparse technique was constructed. Considering the complex periodicity of traffic flow data and the increased error accumulation and model complexity brought by the extended time span in long-term forecasting, cross period sparse technique was used for downsampling processing of the time series. The long-term sequence was decomposed into periods and then spliced and transposed to form a time series matrix. A mixed graph convolutional network was embedded into gated recurrent units to process the downsampled time series matrix, and finally, a time multi-head attention module with a time-dependent matrix was used to fuse relevant spatiotemporal features. After processing three public traffic flow datasets, PeMS-bay, PeMS04, and PeMS08, for effectiveness validation, the results show that the model's prediction accuracy indicators on all three datasets are superior to the baseline model, with average prediction accuracy improved by 5.59%, 8.56%, and 7.11% respectively on the three datasets, and the model parameters is between 14.81% and 27.17% of the baseline model.