Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (6A): 220200042-7.doi: 10.11896/jsjkx.220200042

• Big Data & Data Science • Previous Articles     Next Articles

Spatial-Temporal Graph-CoordAttention Network for Traffic Forecasting

LIU Jiansong, KANG Yan, LI Hao, WANG Tao, WANG Hailing   

  1. School of Software,Yunnan University,Kunming 650504,China
  • Online:2023-06-10 Published:2023-06-12
  • About author:LIU Jiansong,born in 1996,postgra-duate.His main research interests include deep learning and machine lear-ning. 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 Provincial Software Engineering Key Laboratory Open Fund Project(2020SE303),Major Special Project of Yunnan Provincial Science and Technology Department:Key Technology Research and Application of Industry Resource Sharing and Business Collaboration for Smart Tourism(202002AD080047),Genetic Engineering of Mate-rials-Metcloud-based Computational Software Development for Integrated Computational Function Modules(2019CLJY06/202002AB080001-6) and Genetic Engineering of Rare Precious Metal Materials in Yunnan Province-Research and Development of High-Throughput Integrated Computing and Data Analysis Technology for Rare Precious Metal Materials and Demonstration Applications(2019ZE001-1,202002AB080001).

PDF (PC)

291

Abstract: Traffic prediction is an important research component of urban intelligent transportation systems to make our travel more efficient and safer.Accurately predicting traffic flow remains a huge challenge due to complex temporal and spatial depen-dencies.In recent years,graph convolutional network(GCN) has shown great potential for traffic prediction,but GCN-based mo-dels tend to focus on capturing temporal and spatial dependencies,ignoring the dynamic correlation between temporal and spatial dependencies and failing to integrate them well.In addition,previous approaches use real-world static traffic networks to construct spatial adjacency matrices,which may ignore the dynamic spatial dependencies.To overcome these limitations and improve the performance of the model,a novel spatial-temporal Graph-CoordAttention network(STGCA) is proposed.Specifically,the spatial-temporal synchronization module is proposed to model the spatial-temporal dependence of the crossing relations at different moments.Then,a dynamic graph learning scheme is proposed to mine potential graph information based on data correlation between traffic flows.Compared with the existing baseline models on four publicly available datasets,STGCA exhibits excellent perfor-mance.

Key words: Traffic flow forecast, Spatial-temporal forecasting, Graph convolution network, Attention mechanism, Spatial-Temporal dependence

CLC Number: 

  • TP181
[1]LIU J,WEI G.A Summary of Traffic Flow Forecasting Methods[J].Journal of Highway and Transportation Research and Development,2004,21(3):82-85.
[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]XIAO L,PENG X,WANG Z,et al.Research on traffic monitoring network and its traffic flow forecast and congestion control model based on wireless sensor networks[C]//2009 International Conference on Measuring Technology and Mechatronics Automation.IEEE,2009:142-147.
[4]LI Y,YU R,SHAHABI C,et al.Diffusion convolutional recurrent neural network:Data-driven traffic forecasting[J].arXiv:1707.01926,2017.
[5]ZHAO L,SONG Y,ZHANG C,et al.T-gcn:A temporal graph convolutional network for traffic prediction[J].IEEE Transactions on Intelligent Transportation Systems,2019,21(9):3848-3858.
[6]BAI J,ZHU J,SONG Y,et al.A3t-gcn:Attention temporalgraph convolutional network for traffic forecasting[J].ISPRS International Journal of Geo-Information,2021,10(7):485.
[7]HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780.
[8]CHO K,VAN MERRINBOER B,GULCEHRE C,et al.Learning phrase representations using RNN encoder-decoder for statistical machine translation[J].arXiv:1406.1078,2014.
[9]WU Z,PAN S,LONG G,et al.Graph wavenet for deep spatial-temporal graph modeling[J].arXiv:1906.00121,2019.
[10]LI M,ZHU Z.Spatial-temporal fusion graph neural networksfor traffic flow forecasting[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2021,35(5):4189-4196.
[11]KAMARIANAKIS Y,PRASTACOS P.Forecasting traffic flow conditions in an urban network:Comparison of multivariate and univariate approaches[J].Transportation Research Record,2003,1857(1):74-84.
[12]AHMED M S,COOK A R.Analysis of freeway traffic time-series data by using Box-Jenkins techniques[M].Transportation Research Board,1979.
[13]WU C H,HO J M,LEE D T.Travel-time prediction with support vector regression[J].IEEE Transactions on Intelligent Transportation Systems,2004,5(4):276-281.
[14]OKUTANI I,STEPHANEDES Y J.Dynamic prediction of traffic volume through Kalman filtering theory[J].Transportation Research Part B:Methodological,1984,18(1):1-11.
[15]VASWANI A,SHAZEER N,PARMAR N,et al.Attention isall you need[C]//Advances in Neural Information Processing Systems.2017:5998-6008.
[16]LIU Y,ZHENG H,FENG X,et al.Short-term traffic flow prediction with Conv-LSTM[C]//2017 9th International Confe-rence on Wireless Communications and Signal Processing(WCSP).IEEE,2017:1-6.
[17]YAO H,WU F,KE J,et al.Deep multi-view spatial-temporal network for taxi demand prediction[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2018:2588-2595.
[18]BRUNA J,ZAREMBA W,SZLAM A,et al.Spectral networks and locally connected networks on graphs[J].arXiv:1312.6203,2013.
[19]DEFFERRARD M,BRESSON X,VANDERGHEYNST P.Convolutional neural networks on graphs with fast localized spectral filtering[J].Advances in Neural Information Processing Systems,2016,29:3844-3852.
[20]KIPF T N,WELLING M.Semi-supervised classification withgraph convolutional networks[J].arXiv:1609.02907,2016.
[21]HAMILTON W,YING Z,LESKOVEC J.Inductive representation learning on large graphs[J].Advances in Neural Information Processing Systems,2017,30:1024-1034.
[22]VELIČKOVIĆ P,CUCURULL G,CASANOVA A,et al.Graph attention networks[J].arXiv:1710.10903,2017.
[23]Spatio-Temporal Graph Convolutional Networks:A Deep Learning Framework for Traffic Forecasting[C]//IJCAI-18.2017:3634-3640.
[24]GUO S,LIN Y,FENG N,et al.Attention based spatial-temporal graph convolutional networks for traffic flow forecasting[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2019:922-929.
[25]WANG X,MA Y,WANG Y,et al.Traffic flow prediction via spatial temporal graph neural network[C]//Proceedings of the Web Conference 2020.2020:1082-1092.
[1] ZHANG Yian, YANG Ying, REN Gang, WANG Gang. Study on Multimodal Online Reviews Helpfulness Prediction Based on Attention Mechanism [J]. Computer Science, 2023, 50(8): 37-44.
[2] TENG Sihang, WANG Lie, LI Ya. Non-autoregressive Transformer Chinese Speech Recognition Incorporating Pronunciation- Character Representation Conversion [J]. Computer Science, 2023, 50(8): 111-117.
[3] WANG Jiahao, ZHONG Xin, LI Wenxiong, ZHAO Dexin. Human Activity Recognition with Meta-learning and Attention [J]. Computer Science, 2023, 50(8): 193-201.
[4] WANG Yu, WANG Zuchao, PAN Rui. Survey of DGA Domain Name Detection Based on Character Feature [J]. Computer Science, 2023, 50(8): 251-259.
[5] YAN Mingqiang, YU Pengfei, LI Haiyan, LI Hongsong. Arbitrary Image Style Transfer with Consistent Semantic Style [J]. Computer Science, 2023, 50(7): 129-136.
[6] MAO Huihui, ZHAO Xiaole, DU Shengdong, TENG Fei, LI Tianrui. Short-term Subway Passenger Flow Forecasting Based on Graphical Embedding of Temporal Knowledge [J]. Computer Science, 2023, 50(7): 213-220.
[7] GAO Xiang, TANG Jiqiang, ZHU Junwu, LIANG Mingxuan, LI Yang. Study on Named Entity Recognition Method Based on Knowledge Graph Enhancement [J]. Computer Science, 2023, 50(6A): 220700153-6.
[8] ZHANG Tao, CHENG Yifei, SUN Xinxu. Graph Attention Networks Based on Causal Inference [J]. Computer Science, 2023, 50(6A): 220600230-9.
[9] CUI Lin, CUI Chenlu, LIU Zhengwei, XUE Kai. Speech Emotion Recognition Based on Improved MFCC and Parallel Hybrid Model [J]. Computer Science, 2023, 50(6A): 220800211-7.
[10] DUAN Jianyong, YANG Xiao, WANG Hao, HE Li, LI Xin. Document-level Relation Extraction of Graph Attention Convolutional Network Based onInter-sentence Information [J]. Computer Science, 2023, 50(6A): 220800189-6.
[11] YANG Xing, SONG Lingling, WANG Shihui. Remote Sensing Image Classification Based on Improved ResNeXt Network Structure [J]. Computer Science, 2023, 50(6A): 220100158-6.
[12] ZHANG Shunyao, LI Huawang, ZHANG Yonghe, WANG Xinyu, DING Guopeng. Image Retrieval Based on Independent Attention Mechanism [J]. Computer Science, 2023, 50(6A): 220300092-6.
[13] LIU Haowei, YAO Jingchi, LIU Bo, BI Xiuli, XIAO Bin. Two-stage Method for Restoration of Heritage Images Based on Muti-scale Attention Mechanism [J]. Computer Science, 2023, 50(6A): 220600129-8.
[14] LI Fan, JIA Dongli, YAO Yumin, TU Jun. Graph Neural Network Few Shot Image Classification Network Based on Residual and Self-attention Mechanism [J]. Computer Science, 2023, 50(6A): 220500104-5.
[15] SUN Kaiwei, WANG Zhihao, LIU Hu, RAN Xue. Maximum Overlap Single Target Tracking Algorithm Based on Attention Mechanism [J]. Computer Science, 2023, 50(6A): 220400023-5.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.