计算机科学 ›› 2023, Vol. 50 ›› Issue (11): 88-96.doi: 10.11896/jsjkx.221000201
刘起东1,2,3, 刘超越1, 邱紫鑫1, 高志敏1,2,3, 郭帅1,2,3, 刘冀钊4, 符明晟5
LIU Qidong1,2,3, LIU Chaoyue1, QIU Zixin1, GAO Zhimin1,2,3, GUO Shuai1,2,3, LIU Jizhao4, FU Mingsheng5
摘要: 作为智能交通系统的关键一环,交通流预测面临着长时预测不准的难题,其主要挑战在于交通流数据本身具有复杂的时空关联。近年来,Transformer的提出使得时序数据预测的研究取得了巨大进展,但将Transformer应用于交通流预测仍然存在以下两个问题:1)静态的注意力机制难以捕获交通流随时间动态变化的时空依赖关系;2)采用自回归的预测方式会引发严重的误差累积现象。针对以上问题,提出了一种基于时间感知Transformer的交通流预测模型。首先,设计了一种新的时间感知注意力机制,可以根据时间特征定制注意力计算方案,从而更精准地反映时空依赖关系;其次,在Transformer的训练阶段舍弃了Teacher Forcing机制,并采用非自回归的预测方式来避免误差累积问题;最后,在两个真实交通数据集上进行实验,实验结果表明,所提方法可以有效捕获交通流的时空依赖,相比最优的基线方法,长时预测性能提升了2.09%~ 4.01%。
中图分类号:
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