计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 46-51.doi: 10.11896/jsjkx.201200184
康雁, 谢思宇, 王飞, 寇勇奇, 徐玉龙, 吴志伟, 李浩
KANG Yan, XIE Si-yu, WANG Fei, KOU Yong-qi, XU Yu-long, WU Zhi-wei, LI Hao
摘要： 随着深度学习的发展,神经网络在各个领域都有着大量的应用,智慧交通系统也不例外。交通流预测是智慧交通系统的基石,是整个交通预测的核心所在。近年来,图卷积神经网络的利用有效地提高了交通预测的性能,如何进一步提高对图的时空特征进行捕获的能力,将会成为热点。为了提升交通预测的精度,提出了一种基于双路信息时空图卷积网络的交通预测模型。首先,针对图卷积网络的交通预测模型在长距离依赖上建模有所不足,并且没有完全挖掘时空图信息之间的隐藏关系以及在时空图结构上还有信息缺失,提出了一种三重池化注意力机制来建模全局上下文信息。通过对图卷积层和时间卷积层各增加并行的三重池化注意力路径,构造了一个双路信息时空卷积层,提升了卷积层的泛化能力及模型捕获长距离依赖的能力,同时让时空卷积层能够很好地捕获时空图结构上的空间和时间特征,从而有效地提升了交通预测性能。在两个公共交通数据集(METR-LA和PEMS-BAY)上的实验结果表明,该模型具有较好的性能。
中图分类号:
| [1]ZHOU J,CUI G,ZHANG Z,et al.Graph neural networks:A review of methods and applications[J].arXiv:1812.08434,2018. [2]ZHANG J,WANG F Y,WANG K,et al.Data-driven intelligent transportation systems:A survey[J].IEEE Transactions on Intelligent Transportation Systems,2011,12(4):1624-1639. [3]WU Z,PAN S,LONG G,et al.Connecting the Dots:Multivariate Time Series Forecasting with Graph Neural Networks[J].arXiv:2005.11650,2020. [4]CAO Y,XU J,LIN S,et al.Gcnet:Non-local networks meet squeeze-excitation networks and beyond[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops.2019. [5]YANG B,KANG Y,LI H,et al.Spatio-temporal expand-and-squeeze networks for crowd flow prediction in metropolis[J].IET Intelligent Transport Systems,2020,14(5):313-322. [6]BAHDANAU D,CHO K,BENGIO Y.Neural machine translation by jointly learning to align and translate[J].arXiv:1409.0473,2014. [7]WILLIAMS B M,HOEL L A.Modeling and forecasting vehicular traffic flow as a seasonal arima process:Theoretical basis and empirical results.Journal of Transportation Engineering,2003,129(6):664-672. [8]CHENG S,LU F,PENGP,et al.Short-term traffic forecasting:An adaptive ST-KNN model that considers spatial heterogeneity[J].Computers Environment and Urban Systems,2018,71(SEP.):186-198. [9]DU L,PEETA S,KIMY H.An adaptive information fusionmodel to predict the short-term link travel time distribution in dynamic traffic networks[J].Transportation Research Part B Methodological,2012,46(1):235-252. [10]FENG N,GUO S N,SONG C,et al.Multi-component spatial-temporal graph convolution networks for traffic flow forecasting[J].Journal of Software,2019(3):759-769. [11]MA X,YU H,WANG Y,et al.Large-scale transportation network congestion evolution prediction using deep learning theory[J].PloS one,2015,10(3):e0119044. [12]YU R,LI Y,SHAHABI C,et al.Deep learning:A generic approach for extreme condition traffic forecasting[C]//Procee-dings of the 2017 SIAM international Conference on Data Mi-ning.Society for Industrial and Applied Mathematics,2017:777-785. [13]SUN S,HUANG R,GAO Y.Network-Scale Traffic Modelingand Forecasting with Graphical Lasso and Neural Networks[J].Journal of Transportation Engineering,2012,138(11):1358-1367. [14]YU H,WU Z,WANG S,et al.Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks[J].Sensors,2017,17(7):1501. [15]LI Y,YU R,SHAHABI C,et al.Diffusion convolutional recurrent neural network:Data-driven traffic forecasting[J].arXiv:1707.01926,2017. [16]YU B,YIN H,ZHU Z.Spatio-temporal graph convolutional networks:A deep learning framework for traffic forecasting[J].arXiv:1709.04875,2017. [17]WANG X,GIRSHICK R,GUPTA A,et al.Non-local neuralnetworks[C]//Proceedings of the IEEE Conference on ComputerVision and Pattern Recognition.2018:7794-7803. [18]HUANG Z,WANG X,HUANG L,et al.Ccnet:Criss-cross attention for semantic segmentation[C]//Proceedings of the IEEE International Conference on Computer Vision.2019:603-612. [19]HU J,SHEN L,SUN G.Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:7132-7141. [20]HU J,SHEN L,ALBANIE S,et al.Gather-excite:Exploitingfeature context in convolutional neural networks[C]//Advances in Neural Information Processing Systems.2018:9401-9411. [21]ZHAO H,ZHANG Y,LIU S,et al.Psanet:Point-wise spatial attention network for scene parsing[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:267-283. [22]WU Z,PAN S,LONG G,et al.Graph WaveNet for Deep Spatial-Temporal Graph Modeling[C]//Twenty-Eighth International Joint Conference on Artificial Intelligence IJCAI-19.2019. [23]MISRA D,NALAMADA T,ARASANIPALAI A U,et al.Rotate to Attend:Convolutional Triplet Attention Module[J].ar-Xiv:2010.03045,2020. [24]SHEN X J,ZHANG J T,HANG D J.Short-term Traffic Flow Prediction Model Based on Gradient Boosting Regression Tree[J].Computer Science,2018,45(6):222-227,264. [25]WOOS,PARK J,LEE J Y,et al.Cbam:Convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:3-19. |
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