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祁舒畅, 刘起东, 刘超越, 徐明亮, 邱紫鑫. GA2T: 结合图注意力网络的交通流预测模型[J]. 计算机辅助设计与图形学学报.
引用本文: 祁舒畅, 刘起东, 刘超越, 徐明亮, 邱紫鑫. GA2T: 结合图注意力网络的交通流预测模型[J]. 计算机辅助设计与图形学学报.
ShuChang QI, QiDong LIU, ChaoYue LIU, MingLiang XU, ZiXin QIU. GA2T: A Traffic Flow Prediction Model Combined with Graph Attention Networks[J]. Journal of Computer-Aided Design & Computer Graphics.
Citation: ShuChang QI, QiDong LIU, ChaoYue LIU, MingLiang XU, ZiXin QIU. GA2T: A Traffic Flow Prediction Model Combined with Graph Attention Networks[J]. Journal of Computer-Aided Design & Computer Graphics.

GA2T: 结合图注意力网络的交通流预测模型

GA2T: A Traffic Flow Prediction Model Combined with Graph Attention Networks

  • 摘要: 交通流预测是智能交通系统的核心组成部分. 针对当前交通流预测方法准确率低的问题, 提出了一种新的交通流预测模型GA2T. 通过构建具有融合式编解码器的Transformer架构对交通数据进行时间建模, 并利用图注意力网络对交通数据进行空间建模, 从而捕获交通流复杂的时空依赖性. 在2个真实交通数据集METR-LA和PEMS-BAY上的实验结果表明,相较于预测效果最佳的基线模型DCRNN, GA2T在3个评价指标(MAE, MAPE, RMSE)上分别降低了0.25, 0.38, 0.89和0.14, 0.34, 0.44, 表明其在同类工作中处于领先的水平, 验证了其可行性及有效性.

     

    Abstract: Traffic flow prediction is the core component of the intelligent transportation system. In view of the low accuracy of the current traffic flow prediction methods, a new traffic flow prediction model GA2T is proposed. By building a Transformer architecture with fused encoder and decoder to model traffic data temporally, and using graph attention networks to model traffic data spatially, the complex spatial-temporal dependencies of traffic flow are captured. The experimental results on two real traffic datasets METR-LA and PEMS-BAY show that compared with the best baseline model DCRNN, GA2T reduces the three evaluation metrics (MAE, MAPE, RMSE) by 0.25, 0.38, 0.89 and 0.14, 0.34, 0.44. This proves the effectiveness and feasibility of GA2T, and verifies its advances in peer works.

     

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