计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 46-51.doi: 10.11896/jsjkx.201200184

• 智能计算 • 上一篇    下一篇

基于双路信息时空图卷积网络的交通预测模型

康雁, 谢思宇, 王飞, 寇勇奇, 徐玉龙, 吴志伟, 李浩   

  1. 云南大学软件学院 昆明650504
  • 出版日期:2021-11-10 发布日期:2021-11-12
  • 通讯作者: 李浩(lihao707@ynu.edu.cn)
  • 作者简介:562530855@qq.com
  • 基金资助:
    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);云南省重大科技专项(202002AB080001);云南大学服务云南行动计划《机场智慧管理平台关键技术研究及实现》(C176240501005);《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)

Traffic Prediction Model Based on Dual Path Information Spatial-Temporal Graph Convolutional Network

KANG Yan, XIE Si-yu, WANG Fei, KOU Yong-qi, XU Yu-long, WU Zhi-wei, LI Hao   

  1. School of Software,Yunnan University,Kunming 650504,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:KANG Yan,born in 1972,Ph.D,associate professor.Her main research interests include transfer learning,deep learning and integrated learning.
    LI Hao,born in 1970,Ph.D,professor.His main research interests include distributed computing,grid and cloud computing.
  • Supported by:
    National Natural Science Foundation of China(61762092),Yunnan Key Laboratory of Software Engineering Open Fund Project(2017SE204),Yunnan Major Science and Technology Project(202002AB080001),Yunnan University Service Yunnan Action Plan “Key Technology Research and Implementation of Airport Smart Management Platform(C176240501005)and Material Genetic Engineering - Metcloud Based Integrated Computing Function Module Computing Software Development (2019CLJY06).

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。

关键词: 交通预测, 图卷积神经网络, 全局上下文建模, 长距离依赖

Abstract: With the development of deep learning,neural network has a large number of applications in various fields,and intelligent transportation system is no exception.Traffic flow forecast is the cornerstone of intelligent traffic system and the core of the whole traffic forecast.In recent years,the use of the graph convolutional neural network has effectively improved the performance of traffic prediction.How to further improve the ability to capture the spatial and temporal characteristics of the graph will become a hot topic.In order to improve the accuracy of traffic prediction,this paper proposes a traffic prediction model based on the convolution network of dual path information spatial-temporal map.First of all,the traffic prediction model based on the graph convolution network has some shortcomings in long-distance dependence modeling,and has not fully mined the hidden relationship between the spatial-temporal diagram information and the missing information in the spatial-temporal diagram structure,so we propose a triple pooling attention mechanism to model the global context information.Based on the figure of each increase in parallel convolution layer and the time convolution triple pooling attention path,we construct a dual path information spatial-temporal convolution layer,enhance the generalization ability of convolution layer,improve the model's ability to capture long distance dependence,and spatial-temporal convolution layer can capture figure characteristics of space and time structure of spacetime,effectively improve the traffic prediction performance.Experimental results on two public transport data sets (METR-LA and PEMS-BAY) show that the proposed model has good performance.

Key words: Traffic forecast, Graph convolutional neural network, Global context modeling, Long distance dependence

中图分类号: 

  • TP181
[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.
[1] 梁浩宏, 古天龙, 宾辰忠, 常亮. 联合学习用户端和项目端知识图谱的个性化推荐[J]. 计算机科学, 2021, 48(5): 109-116.
[2] 王文博, 罗恒利. 基于图卷积神经网络的完全图人脸聚类[J]. 计算机科学, 2021, 48(11A): 275-277.
[3] 高创, 李建华, 季秀怡, 朱程龙, 李诗良, 李洪林. 基于图卷积神经网络的药物靶标作用关系预测方法[J]. 计算机科学, 2021, 48(10): 127-134.
[4] 刘海潮, 王莉. 基于深度图卷积胶囊网络的图分类模型[J]. 计算机科学, 2020, 47(9): 219-225.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 冯芙蓉, 张兆功. 目标轮廓检测技术新进展[J]. 计算机科学, 2021, 48(6A): 1 -9 .
[2] 孙正, 张小雪. 生物光声成像中声反射伪影抑制方法的研究进展[J]. 计算机科学, 2021, 48(6A): 10 -14 .
[3] 周欣, 刘硕迪, 潘薇, 陈媛媛. 自然交通场景中的车辆颜色识别[J]. 计算机科学, 2021, 48(6A): 15 -20 .
[4] 黄雪冰, 魏佳艺, 沈文宇, 凌力. 基于自适应加权重复值滤波和同态滤波的MR图像增强[J]. 计算机科学, 2021, 48(6A): 21 -27 .
[5] 江妍, 马瑜, 梁远哲, 王原, 李光昊, 马鼎. 基于分数阶麻雀搜索优化OTSU肺组织分割算法[J]. 计算机科学, 2021, 48(6A): 28 -32 .
[6] 冯霞, 胡志毅, 刘才华. 跨模态检索研究进展综述[J]. 计算机科学, 2021, 48(8): 13 -23 .
[7] 周文辉, 石敏, 朱登明, 周军. 基于残差注意力网络的地震数据超分辨率方法[J]. 计算机科学, 2021, 48(8): 24 -31 .
[8] 朝乐门, 尹显龙. 人工智能治理理论及系统的现状与趋势[J]. 计算机科学, 2021, 48(9): 1 -8 .
[9] 雷羽潇, 段玉聪. 面向跨模态隐私保护的AI治理法律技术化框架[J]. 计算机科学, 2021, 48(9): 9 -20 .
[10] 王俊, 王修来, 庞威, 赵鸿飞. 面向科技前瞻预测的大数据治理研究[J]. 计算机科学, 2021, 48(9): 36 -42 .