Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting

Abstract Accurate and real-time traffic passenger flows forecasting at transportation hubs, such as subway/bus stations, is a practical application and of great significance for urban traffic planning, control, guidance, etc. Recently deep learning based methods are promised to learn the spatial-temporal features from high non-linearity and complexity of traffic flows. However, it is still very challenging to handle so much complex factors including the urban transportation network topological structures and the laws of traffic flows with spatial and temporal dependencies. Considering both the static hybrid urban transportation network structures and dynamic spatial-temporal relationships among stations from historical traffic passenger flows, a more effective and fine-grained spatial-temporal features learning framework is necessary. In this paper, we propose a novel spatial-temporal incidence dynamic graph neural networks framework for urban traffic passenger flows prediction. We first model dynamic traffic station relationships over time as spatial-temporal incidence dynamic graph structures based on historically traffic passenger flows. Then we design a novel dynamic graph recurrent convolutional neural network, namely Dynamic-GRCNN, to learn the spatial-temporal features representation for urban transportation network topological structures and transportation hubs. To fully utilize the historical passenger flows, we sample the short-term, medium-term and long-term historical traffic data in training, which can capture the periodicity and trend of the traffic passenger flows at different stations. We conduct extensive experiments on different types of traffic passenger flows datasets including subway, taxi and bus flows in Beijing. The results show that the proposed Dynamic-GRCNN effectively captures comprehensive spatial-temporal correlations significantly and outperforms both traditional and deep learning based urban traffic passenger flows prediction methods.

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

[2]  Philip S. Yu,et al.  Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification , 2019, IEEE Transactions on Knowledge and Data Engineering.

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

[4]  Yu Zheng,et al.  Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction , 2016, AAAI.

[5]  Michael J Demetsky,et al.  TRAFFIC FLOW FORECASTING: COMPARISON OF MODELING APPROACHES , 1997 .

[6]  Jianxin Li,et al.  Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN , 2018, WWW.

[7]  Li Pan,et al.  Predicting Short-Term Traffic Flow by Long Short-Term Memory Recurrent Neural Network , 2015, 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity).

[8]  Linpeng Huang,et al.  Predicting Multi-step Citywide Passenger Demands Using Attention-based Neural Networks , 2018, WSDM.

[9]  Ramez Elmasri,et al.  Scalable deep traffic flow neural networks for urban traffic congestion prediction , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[10]  Billy M. Williams,et al.  Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results , 2003, Journal of Transportation Engineering.

[11]  Mohamed Abdel-Aty,et al.  Development of Artificial Neural Network Models to Predict Driver Injury Severity in Traffic Accidents at Signalized Intersections , 2001 .

[12]  I Okutani,et al.  Dynamic prediction of traffic volume through Kalman Filtering , 1984 .

[13]  Axel Schulz,et al.  The Car that Hit The Burning House: Understanding Small Scale Incident Related Information in Microblogs , 2013, Proceedings of the International AAAI Conference on Web and Social Media.

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

[15]  Cyrus Shahabi,et al.  Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting , 2017, ICLR.

[16]  Jing Li,et al.  Graph CNNs for Urban Traffic Passenger Flows Prediction , 2018, 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI).

[17]  Nicholas G. Polson,et al.  Deep learning for short-term traffic flow prediction , 2016, 1604.04527.

[18]  Md. Zakirul Alam Bhuiyan,et al.  Deep Convolutional Mesh RNN for Urban Traffic Passenger Flows Prediction , 2018, 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI).

[19]  Peter C. Y. Chen,et al.  LSTM network: a deep learning approach for short-term traffic forecast , 2017 .

[20]  Haitham Al-Deek,et al.  Predictions of Freeway Traffic Speeds and Volumes Using Vector Autoregressive Models , 2009, J. Intell. Transp. Syst..

[21]  Chang-Tien Lu,et al.  Urban Traffic Flow Prediction Using a Spatio-Temporal Random Effects Model , 2016, J. Intell. Transp. Syst..

[22]  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).

[23]  Bo Peng,et al.  Short‐term traffic flow prediction with linear conditional Gaussian Bayesian network , 2016 .

[24]  Jianxin Li,et al.  Road Traffic Speed Prediction: A Probabilistic Model Fusing Multi-Source Data , 2018, IEEE Transactions on Knowledge and Data Engineering.

[25]  Jieping Ye,et al.  Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction , 2018, AAAI.

[26]  Zhoujun Li,et al.  Citywide traffic congestion estimation with social media , 2015, SIGSPATIAL/GIS.

[27]  Jianqiang Huang,et al.  Dynamic Spatio-temporal Graph-based CNNs for Traffic Prediction , 2018, ArXiv.

[28]  Md Zakirul Alam Bhuiyan,et al.  Deep Irregular Convolutional Residual LSTM for Urban Traffic Passenger Flows Prediction , 2020, IEEE Transactions on Intelligent Transportation Systems.

[29]  Mathias Niepert,et al.  Learning Convolutional Neural Networks for Graphs , 2016, ICML.

[30]  Xiaoming Zhang,et al.  Computing Urban Traffic Congestions by Incorporating Sparse GPS Probe Data and Social Media Data , 2017, ACM Trans. Inf. Syst..

[31]  Quoc V. Le,et al.  On optimization methods for deep learning , 2011, ICML.

[32]  Tianrui Li,et al.  Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks , 2017, Artif. Intell..

[33]  Billy M. Williams,et al.  Comparison of parametric and nonparametric models for traffic flow forecasting , 2002 .

[34]  Hashem R Al-Masaeid,et al.  Short-Term Prediction of Traffic Volume in Urban Arterials , 1995 .

[35]  Hui Xiong,et al.  Catch Me If You Can: Detecting Pickpocket Suspects from Large-Scale Transit Records , 2016, KDD.

[36]  Javier A. Barria,et al.  Lane-level traffic estimations using microscopic traffic variables , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[37]  T. Maze,et al.  Whether Weather Matters to Traffic Demand, Traffic Safety, and Traffic Operations and Flow , 2006 .

[38]  Chen Wang,et al.  Applied research of data sensing and service to ubiquitous intelligent transportation system , 2010, Frontiers of Computer Science in China.

[39]  Billy M. Williams,et al.  Urban Freeway Traffic Flow Prediction: Application of Seasonal Autoregressive Integrated Moving Average and Exponential Smoothing Models , 1998 .

[40]  Rob Hranac,et al.  Twitter Interactions as a Data Source for Transportation Incidents , 2013 .

[41]  Zhoujun Li,et al.  Estimating Urban Traffic Congestions with Multi-sourced Data , 2016, 2016 17th IEEE International Conference on Mobile Data Management (MDM).

[42]  Zhanxing Zhu,et al.  Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting , 2017, IJCAI.

[43]  Dit-Yan Yeung,et al.  Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.

[44]  Fei-Yue Wang,et al.  Traffic Flow Prediction With Big Data: A Deep Learning Approach , 2015, IEEE Transactions on Intelligent Transportation Systems.