Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction

Intelligent transportation systems (ITSs) have become an indispensable component of modern global technological development, as they play a massive role in the accurate statistical estimation of vehicles or individuals commuting to a particular transportation facility at a given time. This provides the perfect backdrop for designing and engineering an adequate infrastructural capacity for transportation analyses. However, traffic prediction remains a daunting task due to the non-Euclidean and complex distribution of road networks and the topological constraints of urbanized road networks. To solve this challenge, this paper presents a traffic forecasting model which combines a graph convolutional network, a gated recurrent unit, and a multi-head attention mechanism to simultaneously capture and incorporate the spatio-temporal dependence and dynamic variation in the topological sequence of traffic data effectively. By achieving 91.8% accuracy on the Los Angeles highway traffic (Los-loop) test data for 15-min traffic prediction and an R2 score of 85% on the Shenzhen City (SZ-taxi) test dataset for 15- and 30-min predictions, the proposed model demonstrated that it can learn the global spatial variation and the dynamic temporal sequence of traffic data over time. This has resulted in state-of-the-art traffic forecasting for the SZ-taxi and Los-loop datasets.

[1]  Yanming Shen,et al.  A Spatial–Temporal Attention Approach for Traffic Prediction , 2021, IEEE Transactions on Intelligent Transportation Systems.

[2]  G ThippaReddy,et al.  CANintelliIDS: Detecting In-Vehicle Intrusion Attacks on a Controller Area Network Using CNN and Attention-Based GRU , 2021, IEEE Transactions on Network Science and Engineering.

[3]  Heriberto Pérez-Acebo,et al.  Managing Traffic Data through Clustering and Radial Basis Functions , 2021, Sustainability.

[4]  Ke Zhang,et al.  Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction , 2021, IEEE Transactions on Intelligent Transportation Systems.

[5]  Haifeng Li,et al.  AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting , 2020, IEEE Access.

[6]  Shuo Zhang,et al.  Spatial-Temporal Dilated and Graph Convolutional Network for traffic prediction , 2020, 2020 Chinese Automation Congress (CAC).

[7]  Junhao Wen,et al.  GST-GCN: A Geographic-Semantic-Temporal Graph Convolutional Network for Context-aware Traffic Flow Prediction on Graph Sequences , 2020, 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[8]  Yun Xiong,et al.  SAST-GNN: A Self-Attention Based Spatio-Temporal Graph Neural Network for Traffic Prediction , 2020, DASFAA.

[9]  Xi Lin,et al.  Graph attention temporal convolutional network for traffic speed forecasting on road networks , 2020, Transportmetrica B: Transport Dynamics.

[10]  Haoyi Niu,et al.  Graph Attention Convolutional Network: Spatiotemporal Modeling for Urban Traffic Prediction , 2020, 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC).

[11]  Hongxin Zhang,et al.  Attention based Graph Covolution Networks for Intelligent Traffic Flow Analysis , 2020, 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE).

[12]  Rui Dai,et al.  Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data , 2020, KDD.

[13]  Haifeng Li,et al.  A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting , 2020, ISPRS Int. J. Geo Inf..

[14]  Hung-yi Lee,et al.  Hand-crafted Attention is All You Need? A Study of Attention on Self-supervised Audio Transformer , 2020, ArXiv.

[15]  Krzysztof Janowicz,et al.  Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting , 2020, Trans. GIS.

[16]  Wei Luo,et al.  Short-Term Traffic Speed Prediction Method for Urban Road Sections Based on Wavelet Transform and Gated Recurrent Unit , 2020 .

[17]  Baocai Yin,et al.  A Comprehensive Survey on Traffic Prediction , 2020, ArXiv.

[18]  Jingqiu Guo,et al.  GPS-based citywide traffic congestion forecasting using CNN-RNN and C3D hybrid model , 2020 .

[19]  Vasiliy Osipov,et al.  Urban traffic flows forecasting by recurrent neural networks with spiral structures of layers , 2020, Neural Computing and Applications.

[20]  Guan Gui,et al.  Enhanced Echo-State Restricted Boltzmann Machines for Network Traffic Prediction , 2020, IEEE Internet of Things Journal.

[21]  Meng Wang,et al.  Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach , 2020, AAAI.

[22]  Zhiheng Li,et al.  Building sparse models for traffic flow prediction: an empirical comparison between statistical heuristics and geometric heuristics for Bayesian network approaches , 2019 .

[23]  Xian-Sheng Hua,et al.  Dual Graph for Traffic Forecasting , 2019, IEEE Access.

[24]  Wei Cao,et al.  Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting , 2019, AAAI.

[25]  Xiaopeng Hong,et al.  Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching , 2019, AAAI.

[26]  Cheng Wang,et al.  GMAN: A Graph Multi-Attention Network for Traffic Prediction , 2019, AAAI.

[27]  Ning Feng,et al.  Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting , 2019, AAAI.

[28]  Bo Yang,et al.  Time to lane change and completion prediction based on Gated Recurrent Unit Network , 2019, 2019 IEEE Intelligent Vehicles Symposium (IV).

[29]  Karim El-Basyouny,et al.  Estimating Traffic Volume on Minor Roads at Rural Stop-Controlled Intersections using Deep Learning , 2019, Transportation Research Record: Journal of the Transportation Research Board.

[30]  Jinde Cao,et al.  Short-Term Traffic Flow Prediction Based on Least Square Support Vector Machine with Hybrid Optimization Algorithm , 2019, Neural Processing Letters.

[31]  Haifeng Li,et al.  Temporal Graph Convolutional Network for Urban Traffic Flow Prediction Method , 2018, ArXiv.

[32]  Qi Zhang,et al.  Kernel-Weighted Graph Convolutional Network: A Deep Learning Approach for Traffic Forecasting , 2018, 2018 24th International Conference on Pattern Recognition (ICPR).

[33]  Pengpeng Zhao,et al.  LC-RNN: A Deep Learning Model for Traffic Speed Prediction , 2018, IJCAI.

[34]  Xianfeng Tang,et al.  Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction , 2018, AAAI.

[35]  Yong Wang,et al.  Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction , 2017, Sensors.

[36]  Xiuwen Yi,et al.  DNN-based prediction model for spatio-temporal data , 2016, SIGSPATIAL/GIS.

[37]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[38]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[39]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[40]  E. E. García-Guerrero,et al.  Traffic Flow Prediction for Smart Traffic Lights Using Machine Learning Algorithms , 2022 .

[41]  Zhiguang Qin,et al.  Features to Text: A Comprehensive Survey of Deep Learning on Semantic Segmentation and Image Captioning , 2021, Complex..

[42]  Lingmin Yang,et al.  Uncertainty prediction method for traffic flow based on K-nearest neighbor algorithm , 2020, J. Intell. Fuzzy Syst..

[43]  Muhammad Umar Aftab,et al.  Attentively Conditioned Generative Adversarial Network for Semantic Segmentation , 2020, IEEE Access.

[44]  Zhiguang Qin,et al.  Fully Convolutional CaptionNet: Siamese Difference Captioning Attention Model , 2019, IEEE Access.

[45]  Yasushi Yokoya,et al.  Prediction of collision risk based on driver’s behavior in anticipation of a traffic accident risk , 2019, The Proceedings of the Transportation and Logistics Conference.

[46]  Eatedal Alabdulkreem,et al.  CaptionNet: Automatic End-to-End Siamese Difference Captioning Model With Attention , 2019, IEEE Access.

[47]  Guan Wei,et al.  A Summary of Traffic Flow Forecasting Methods , 2004 .

[48]  Yoshua Bengio,et al.  Object Recognition with Gradient-Based Learning , 1999, Shape, Contour and Grouping in Computer Vision.

[49]  David A. Forsyth,et al.  Shape, Contour and Grouping in Computer Vision , 1999, Lecture Notes in Computer Science.

[50]  A. R. Cook,et al.  ANALYSIS OF FREEWAY TRAFFIC TIME-SERIES DATA BY USING BOX-JENKINS TECHNIQUES , 1979 .