计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 159-165.doi: 10.11896/jsjkx.201200051
李浩, 王飞, 谢思宇, 寇勇奇, 张兰, 杨兵, 康雁
LI Hao, WANG Fei, XIE Si-yu, KOU Yong-qi, ZHANG Lan, YANG Bing, KANG Yan
摘要： 随着智慧城市的建设,城市交通流量预测在智能交通预警和交通管理决策方面至关重要。由于复杂的时空相关性,有效地对交通流量进行预测成为了一项挑战。现有的对交通流量进行预测的方法大多采用机器学习算法或深度学习模型,而它们各有优缺点,若能够将两者优点结合起来,将进一步提高交通流量预测的精度。文中针对交通时空数据,提出了一种基于改进图波网(Graph WaveNet)的双重自回归分量交通预测模型。首先,通过门控3分支时间卷积网络有效融合3个时间卷积层,从而进一步提升了捕获时间相关性的能力;其次,首次引入自回归分量,将自回归分量和门控三分支时间卷积网络、图卷积层有效融合,使模型能够充分反映时空数据之间的线性和非线性关系。在METR-LA和PEMS-BAY两个真实的公共交通数据集上进行实验,并将所提模型与其他交通流量预测基准模型进行比较。结果表明,不管是短时间还是长时间的预测,文中所提模型在各个指标上都优于基准模型。
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