一种基于改进图波网的双重自回归分量交通预测模型  

Dual Autoregressive Components Traffic Prediction Based on Improved Graph WaveNet

作　　者：李浩[1] 王飞 谢思宇 寇勇奇 张兰 杨兵 康雁[1] LI Hao;WANG Fei;XIE Si-yu;KOU Yong-qi;ZHANG Lan;YANG Bing;KANG Yan(School of Software,Yunnan University,Kunming 650504,China)

机构地区：[1]云南大学软件学院,昆明650504

出　　处：《计算机科学》2021年第S02期159-165,183,共8页Computer Science

基　　金：国家自然科学基金(61762092);云南省软件工程重点实验室开放基金项目(2017SE204);云南省软件工程重点实验室(2020SE303);云南省重大科技专项(202002AB080001);《材料基因工程-基于Metcloud的集成计算功能模块计算软件开发》(2019CLJY06);云南省稀贵金属材料基因工程(2019ZE001-1,202002AB080001)。

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。With the construction of smart cities,urban traffic flow forecasting is crucial in intelligent traffic early warning and traffic management decision-making.Due to the complex temporal and spatial correlation,it is a challenge to effectively predict traffic flow.Most of the existing traffic flow prediction methods use machine learning algorithms or deep learning models,and the methods have their own advantages and disadvantages.If the two advantages can be combined,the accuracy of traffic flow prediction will be further improved.Aiming at the traffic spatio-temporal data,a dual autoregressive component traffic prediction model based on improved Graph WaveNet is proposed.First,the three time convolution layers are effectively fused through the gated three-branch time convolution network,which further improves the ability of capture time correlation.Second,the autoregression component is introduced for the first time to effectively fuse the autoregression component with the gated three-branch time convolution network and the convolution layer,so that the model can fully reflect the linear and non-linear relationship between space-time data.Through experiments on two real public transportation data sets of METR-LA and PEMS-BAY,the proposed model is compared with other traffic flow prediction benchmark models.The results show that whether it is a short-term or longterm forecast,the model proposed in the paper is better than the benchmark model in all indicators.

关 键 词：智能交通 时空数据 交通流量预测 自回归分量 时间卷积层 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]