Computer Science ›› 2022, Vol. 49 ›› Issue (8): 40-48.doi: 10.11896/jsjkx.220100188

• Database & Big Data & Data Science • Previous Articles     Next Articles

Multi-time Scale Spatial-Temporal Graph Neural Network for Traffic Flow Prediction

WANG Ming, PENG Jian, HUANG Fei-hu   

  1. College of Computer Science,Sichuan University,Chengdu 610041,China
  • Received:2022-01-19 Revised:2022-02-23 Published:2022-08-02
  • About author:WANG Ming,born in 1997,postgra-duate,is a student member of China Computer Federation.His main research interests include graph neural network and time-series prediction.
    PENG Jian,born in 1970,Ph.D,professor,Ph.D supervisor,is a outstanding member of China Computer Federation.His main research interests include big data and wireless sensor network.
  • Supported by:
    National Key R & D Program of China(2020YFB0704502) and Key R & D Program of Sichuan Province,China(2020YFG0089,2020YFG0308,2020YFG0304,22ZDYF3599).

Abstract: Traffic flow prediction plays a key role in intelligent transportation system.However,due to its complex spatial-temporal dependence and its uncertainty,research becomes extremely challenging.Some existing methods mainly use a single time series and input it into a recurrent neural network to capture temporal dependency.Moreover,most models only simply stack temporal modules and spatial modules,resulting in ineffective feature fusion.To address these issues,this paper proposes a multi-time scale spatial-temporal graph neural network model.The model divides the sequence data into three time-scale sequences,then puts sequence data into the ST-Blocks to extract the spatial-temporal features of data,and finally makes the prediction.In ST-Block,graph convolutional network and variant Transformer are used to capture spatial dependency and temporal dependency respectively,and the output feature of the two sub-modules are fused through a gate mechanism.A large number of experiments are conducted on two real data sets in this paper,including short-term and long-term prediction,and the results show the excellent performance of MTSTGNN model on the task of traffic flow prediction.

Key words: Attention mechanism, Gate mechanism, Graph convolutional network, Traffic flow prediction, Transformer

CLC Number: 

  • TP183
[1]XU H B,DENG H J.Application of artificial intelligence technology in urban intelligent transportation system [J].Computer Products and Circulation,2020(1):167.
[2]WILLIAMS B M,HOEL L A.Modeling and forecasting vehicular traffic flow as a seasonal stochastic time series process[J].Dissertation Abstracts International,1999,60(1):1-5.
[3]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.
[4]LINT H V,HINSBERGEN C V.Short-Term Traffic and Travel Time Prediction Models[J].Transportation Research E-Circular,2012,45(7):635-641.
[5]DRUCKER H,BURGES C J C,KAUFMAN L,et al.SupportVector Regression Machines[J].Advances in Neural Information Processing Systems,1997,28(7):779-784.
[6]FU R,ZHANG Z,LI L.Using LSTM and GRU neural network methods for traffic flow prediction[C]//2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC).2016:324-328.
[7]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).2017:1-6.
[8]LI Y,YU R,SHAHABI C,et al.Diffusion convolutional recurrent neural network:Data-driven traffic forecasting[J].arXiv:1707.01926,2017.
[9]YU B,YIN H,ZHU Z.Spatio-temporal graph convolutional networks:A deep learning framework for traffic forecasting[J].arXiv:1709.04875,2017.
[10]XU M,DAI W,LIU C,et al.Spatial-temporal transformer networks for traffic flow forecasting[J].arXiv:2001.02908,2020.
[11]ZHOU M.Research on short-term traffic flow prediction me-thod based on road network spatiotemporal information[D].Beijing:North China University of Technology,2020.
[12]NIEPERT M,AHMED M,KUTZKOV K.Learning convolu-tional neural networks for graphs[C]//International Conference on Machine Learning.2016:2014-2023.
[13]WU Z,PAN S,CHEN F,et al.A comprehensive survey ongraph neural networks[J].IEEE Transactions on Neural Networks and Learning Systems,2020,32(1):4-24.
[14]BRUNA J,ZAREMBA W,SZLAM A,et al.Spectral networks and locally connected networks on graphs[J].arXiv:1312.6203,2013.
[15]DEFFERRARD M,BRESSON X,VAND P.Convolutional neural networks on graphs with fast localized spectral filtering[J].Advances in Neural Information Processing Systems,2016,29:3844-3852.
[16]BAHDANAU D,CHO K,BENGIO Y.Neural machine translation by jointly learning to align and translate[J].arXiv:1409.0473,2014.
[17]VASWANI A,SHAZEER N,PARMAR N,et al.Attention isall you need[C]//Advances in Neural Information Processing Systems.2017:5998-6008.
[18]ZHOU H,ZHANG S,PENG J,et al.Informer:Beyond efficient transformer for long sequence time-series forecasting[C]//Proceedings of AAAI.2021.
[19]WU H,XU J,WANG J,et al.Autoformer:Decomposition transformers with auto-correlation for long-term series forecasting[J].Advances in Neural Information Processing Systems,2021,34:22419-22430.
[20]ZHENG C,FAN X,WANG C,et al.Gman:A graph multi-attention network for traffic prediction[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2020:1234-1241.
[21]CHEN W,CHEN L,XIE Y,et al.Multi-range attentive bicomponent graph convolutional network for traffic forecasting[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2020:3529-3536.
[22]PARK C,LEE C,BAHNG H,et al.ST-GRAT:A novel spatio-temporal graph attention networks for accurately forecasting dynamically changing road speed[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management.2020:1215-1224.
[23]FAWAZ H I,FORESTIER G,WEBER J,et al.Data augmentation using synthetic data for time series classification with deep residual networks[J].arXiv:1808.02455,2018.
[1] ZHOU Fang-quan, CHENG Wei-qing. Sequence Recommendation Based on Global Enhanced Graph Neural Network [J]. Computer Science, 2022, 49(9): 55-63.
[2] DAI Yu, XU Lin-feng. Cross-image Text Reading Method Based on Text Line Matching [J]. Computer Science, 2022, 49(9): 139-145.
[3] ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161.
[4] XIONG Li-qin, CAO Lei, LAI Jun, CHEN Xi-liang. Overview of Multi-agent Deep Reinforcement Learning Based on Value Factorization [J]. Computer Science, 2022, 49(9): 172-182.
[5] RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207.
[6] ZHU Cheng-zhang, HUANG Jia-er, XIAO Ya-long, WANG Han, ZOU Bei-ji. Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism [J]. Computer Science, 2022, 49(8): 113-119.
[7] SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177.
[8] TAN Ying-ying, WANG Jun-li, ZHANG Chao-bo. Review of Text Classification Methods Based on Graph Convolutional Network [J]. Computer Science, 2022, 49(8): 205-216.
[9] YAN Jia-dan, JIA Cai-yan. Text Classification Method Based on Information Fusion of Dual-graph Neural Network [J]. Computer Science, 2022, 49(8): 230-236.
[10] JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335.
[11] ZHANG Ying-tao, ZHANG Jie, ZHANG Rui, ZHANG Wen-qiang. Photorealistic Style Transfer Guided by Global Information [J]. Computer Science, 2022, 49(7): 100-105.
[12] ZENG Zhi-xian, CAO Jian-jun, WENG Nian-feng, JIANG Guo-quan, XU Bin. Fine-grained Semantic Association Video-Text Cross-modal Entity Resolution Based on Attention Mechanism [J]. Computer Science, 2022, 49(7): 106-112.
[13] XU Ming-ke, ZHANG Fan. Head Fusion:A Method to Improve Accuracy and Robustness of Speech Emotion Recognition [J]. Computer Science, 2022, 49(7): 132-141.
[14] MENG Yue-bo, MU Si-rong, LIU Guang-hui, XU Sheng-jun, HAN Jiu-qiang. Person Re-identification Method Based on GoogLeNet-GMP Based on Vector Attention Mechanism [J]. Computer Science, 2022, 49(7): 142-147.
[15] JIN Fang-yan, WANG Xiu-li. Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM [J]. Computer Science, 2022, 49(7): 179-186.
Full text



No Suggested Reading articles found!